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对结构化数据进行分类
原文：https://tensorflow.google.cn/tutorials/structured_data/feature_columns
Note: 我们的 TensorFlow 社区翻译了这些文档。因为社区翻译是尽力而为， 所以无法保证它们是最准确的，并且反映了最新的 官方英文文档。如果您有改进此翻译的建议， 请提交 pull request 到 tensorflow/docs GitHub 仓库。要志愿地撰写或者审核译文，请加入 docs-zh-cn@tensorflow.org Google Group。
本教程演示了如何对结构化数据进行分类（例如，CSV 中的表格数据）。我们将使用 Keras 来定义模型，将特征列（feature columns） 作为从 CSV 中的列（columns）映射到用于训练模型的特征（features）的桥梁。本教程包括了以下内容的完整代码：
- 用 Pandas 导入 CSV 文件。
- 用 tf.data 建立了一个输入流水线（pipeline），用于对行进行分批（batch）和随机排序（shuffle）。
- 用特征列将 CSV 中的列映射到用于训练模型的特征。
- 用 Keras 构建，训练并评估模型。
数据集
我们将使用一个小型 数据集，该数据集由克利夫兰心脏病诊所基金会（Cleveland Clinic Foundation for Heart Disease）提供。CSV 中有几百行数据。每行描述了一个病人（patient），每列描述了一个属性（attribute）。我们将使用这些信息来预测一位病人是否患有心脏病，这是在该数据集上的二分类任务。
下面是该数据集的描述。 请注意，有数值（numeric）和类别（categorical）类型的列。
列 描述 特征类型 数据类型 Age 年龄以年为单位 Numerical integer Sex （1 = 男；0 = 女） Categorical integer CP 胸痛类型（0，1，2，3，4） Categorical integer Trestbpd 静息血压（入院时，以 mm Hg 计） Numerical integer Chol 血清胆固醇（mg/dl） Numerical integer FBS （空腹血糖> 120 mg/dl）（1 = true；0 = false） Categorical integer RestECG 静息心电图结果（0，1，2） Categorical integer Thalach 达到的最大心率 Numerical integer Exang 运动诱发心绞痛（1 =是；0 =否） Categorical integer Oldpeak 与休息时相比由运动引起的 ST 节段下降 Numerical integer Slope 在运动高峰 ST 段的斜率 Numerical float CA 荧光透视法染色的大血管动脉（0-3）的数量 Numerical integer Thal 3 =正常；6 =固定缺陷；7 =可逆缺陷 Categorical string Target 心脏病诊断（1 = true；0 = false） Classification integer
导入 TensorFlow 和其他库
pip install -q sklearn
WARNING: You are using pip version 20.2.2; however, version 20.2.3 is available.
You should consider upgrading via the '/tmpfs/src/tf_docs_env/bin/python -m pip install --upgrade pip' command.
import numpy as np
import pandas as pd
import tensorflow as tf
from tensorflow import feature_column
from tensorflow.keras import layers
from sklearn.model_selection import train_test_split
使用 Pandas 创建一个 dataframe
Pandas 是一个 Python 库，它有许多有用的实用程序，用于加载和处理结构化数据。我们将使用 Pandas 从 URL 下载数据集，并将其加载到 dataframe 中。
URL = 'https://storage.googleapis.com/applied-dl/heart.csv'
dataframe = pd.read_csv(URL)
dataframe.head()
将 dataframe 拆分为训练、验证和测试集
我们下载的数据集是一个 CSV 文件。 我们将其拆分为训练、验证和测试集。
train, test = train_test_split(dataframe, test_size=0.2)
train, val = train_test_split(train, test_size=0.2)
print(len(train), 'train examples')
print(len(val), 'validation examples')
print(len(test), 'test examples')
193 train examples
49 validation examples
61 test examples
用 tf.data 创建输入流水线
接下来，我们将使用 tf.data 包装 dataframe。这让我们能将特征列作为一座桥梁，该桥梁将 Pandas dataframe 中的列映射到用于训练模型的特征。如果我们使用一个非常大的 CSV 文件（非常大以至于它不能放入内存），我们将使用 tf.data 直接从磁盘读取它。本教程不涉及这一点。
# 一种从 Pandas Dataframe 创建 tf.data 数据集的实用程序方法（utility method）
def df_to_dataset(dataframe, shuffle=True, batch_size=32):
dataframe = dataframe.copy()
labels = dataframe.pop('target')
ds = tf.data.Dataset.from_tensor_slices((dict(dataframe), labels))
if shuffle:
ds = ds.shuffle(buffer_size=len(dataframe))
ds = ds.batch(batch_size)
return ds
batch_size = 5 # 小批量大小用于演示
train_ds = df_to_dataset(train, batch_size=batch_size)
val_ds = df_to_dataset(val, shuffle=False, batch_size=batch_size)
test_ds = df_to_dataset(test, shuffle=False, batch_size=batch_size)
理解输入流水线
现在我们已经创建了输入流水线，让我们调用它来查看它返回的数据的格式。 我们使用了一小批量大小来保持输出的可读性。
for feature_batch, label_batch in train_ds.take(1):
print('Every feature:', list(feature_batch.keys()))
print('A batch of ages:', feature_batch['age'])
print('A batch of targets:', label_batch )
Every feature: ['age', 'sex', 'cp', 'trestbps', 'chol', 'fbs', 'restecg', 'thalach', 'exang', 'oldpeak', 'slope', 'ca', 'thal']
A batch of ages: tf.Tensor([58 44 44 50 54], shape=(5,), dtype=int64)
A batch of targets: tf.Tensor([0 1 0 0 1], shape=(5,), dtype=int64)
我们可以看到数据集返回了一个字典，该字典从列名称（来自 dataframe）映射到 dataframe 中行的列值。
演示几种特征列
TensorFlow 提供了多种特征列。本节中，我们将创建几类特征列，并演示特征列如何转换 dataframe 中的列。
# 我们将使用该批数据演示几种特征列
example_batch = next(iter(train_ds))[0]
# 用于创建一个特征列
# 并转换一批次数据的一个实用程序方法
def demo(feature_column):
feature_layer = layers.DenseFeatures(feature_column)
print(feature_layer(example_batch).numpy())
数值列
一个特征列的输出将成为模型的输入（使用上面定义的 demo 函数，我们将能准确地看到 dataframe 中的每列的转换方式）。 数值列（numeric column） 是最简单的列类型。它用于表示实数特征。使用此列时，模型将从 dataframe 中接收未更改的列值。
age = feature_column.numeric_column("age")
demo(age)
WARNING:tensorflow:Layer dense_features is casting an input tensor from dtype float64 to the layer's dtype of float32, which is new behavior in TensorFlow 2\. The layer has dtype float32 because its dtype defaults to floatx.
If you intended to run this layer in float32, you can safely ignore this warning. If in doubt, this warning is likely only an issue if you are porting a TensorFlow 1.X model to TensorFlow 2.
To change all layers to have dtype float64 by default, call `tf.keras.backend.set_floatx('float64')`. To change just this layer, pass dtype='float64' to the layer constructor. If you are the author of this layer, you can disable autocasting by passing autocast=False to the base Layer constructor.
[[63.]
[62.]
[59.]
[74.]
[68.]]
在这个心脏病数据集中，dataframe 中的大多数列都是数值列。
分桶列
通常，您不希望将数字直接输入模型，而是根据数值范围将其值分成不同的类别。考虑代表一个人年龄的原始数据。我们可以用 分桶列（bucketized column）将年龄分成几个分桶（buckets），而不是将年龄表示成数值列。请注意下面的 one-hot 数值表示每行匹配的年龄范围。
age_buckets = feature_column.bucketized_column(age, boundaries=[18, 25, 30, 35, 40, 45, 50, 55, 60, 65])
demo(age_buckets)
WARNING:tensorflow:Layer dense_features_1 is casting an input tensor from dtype float64 to the layer's dtype of float32, which is new behavior in TensorFlow 2\. The layer has dtype float32 because its dtype defaults to floatx.
If you intended to run this layer in float32, you can safely ignore this warning. If in doubt, this warning is likely only an issue if you are porting a TensorFlow 1.X model to TensorFlow 2.
To change all layers to have dtype float64 by default, call `tf.keras.backend.set_floatx('float64')`. To change just this layer, pass dtype='float64' to the layer constructor. If you are the author of this layer, you can disable autocasting by passing autocast=False to the base Layer constructor.
[[0\. 0\. 0\. 0\. 0\. 0\. 0\. 0\. 0\. 1\. 0.]
[0\. 0\. 0\. 0\. 0\. 0\. 0\. 0\. 0\. 1\. 0.]
[0\. 0\. 0\. 0\. 0\. 0\. 0\. 0\. 1\. 0\. 0.]
[0\. 0\. 0\. 0\. 0\. 0\. 0\. 0\. 0\. 0\. 1.]
[0\. 0\. 0\. 0\. 0\. 0\. 0\. 0\. 0\. 0\. 1.]]
分类列
在此数据集中，thal 用字符串表示（如 'fixed'，'normal'，或 'reversible'）。我们无法直接将字符串提供给模型。相反，我们必须首先将它们映射到数值。分类词汇列（categorical vocabulary columns）提供了一种用 one-hot 向量表示字符串的方法（就像您在上面看到的年龄分桶一样）。词汇表可以用 categorical_column_with_vocabulary_list 作为 list 传递，或者用 categorical_column_with_vocabulary_file 从文件中加载。
thal = feature_column.categorical_column_with_vocabulary_list(
'thal', ['fixed', 'normal', 'reversible'])
thal_one_hot = feature_column.indicator_column(thal)
demo(thal_one_hot)
WARNING:tensorflow:Layer dense_features_2 is casting an input tensor from dtype float64 to the layer's dtype of float32, which is new behavior in TensorFlow 2\. The layer has dtype float32 because its dtype defaults to floatx.
If you intended to run this layer in float32, you can safely ignore this warning. If in doubt, this warning is likely only an issue if you are porting a TensorFlow 1.X model to TensorFlow 2.
To change all layers to have dtype float64 by default, call `tf.keras.backend.set_floatx('float64')`. To change just this layer, pass dtype='float64' to the layer constructor. If you are the author of this layer, you can disable autocasting by passing autocast=False to the base Layer constructor.
[[0\. 0\. 1.]
[0\. 0\. 1.]
[1\. 0\. 0.]
[0\. 1\. 0.]
[0\. 0\. 1.]]
在更复杂的数据集中，许多列都是分类列（如 strings）。在处理分类数据时，特征列最有价值。尽管在该数据集中只有一列分类列，但我们将使用它来演示在处理其他数据集时，可以使用的几种重要的特征列。
嵌入列
假设我们不是只有几个可能的字符串，而是每个类别有数千（或更多）值。 由于多种原因，随着类别数量的增加，使用 one-hot 编码训练神经网络变得不可行。我们可以使用嵌入列来克服此限制。嵌入列（embedding column）将数据表示为一个低维度密集向量，而非多维的 one-hot 向量，该低维度密集向量可以包含任何数，而不仅仅是 0 或 1。嵌入的大小（在下面的示例中为 8）是必须调整的参数。
关键点：当分类列具有许多可能的值时，最好使用嵌入列。我们在这里使用嵌入列用于演示目的，为此您有一个完整的示例，以在将来可以修改用于其他数据集。
# 注意到嵌入列的输入是我们之前创建的类别列
thal_embedding = feature_column.embedding_column(thal, dimension=8)
demo(thal_embedding)
WARNING:tensorflow:Layer dense_features_3 is casting an input tensor from dtype float64 to the layer's dtype of float32, which is new behavior in TensorFlow 2\. The layer has dtype float32 because its dtype defaults to floatx.
If you intended to run this layer in float32, you can safely ignore this warning. If in doubt, this warning is likely only an issue if you are porting a TensorFlow 1.X model to TensorFlow 2.
To change all layers to have dtype float64 by default, call `tf.keras.backend.set_floatx('float64')`. To change just this layer, pass dtype='float64' to the layer constructor. If you are the author of this layer, you can disable autocasting by passing autocast=False to the base Layer constructor.
[[ 0.00543996 -0.5059579 0.0389499 -0.20236802 0.11128058 0.59121
0.14891742 -0.11942385]
[ 0.00543996 -0.5059579 0.0389499 -0.20236802 0.11128058 0.59121
0.14891742 -0.11942385]
[ 0.09787773 -0.5861865 -0.3713007 -0.1747458 -0.01538717 0.55458224
0.12537968 -0.11748305]
[-0.00701649 0.28291813 0.23547529 -0.5102454 -0.5388726 0.5154376
0.12235989 0.44484815]
[ 0.00543996 -0.5059579 0.0389499 -0.20236802 0.11128058 0.59121
0.14891742 -0.11942385]]
经过哈希处理的特征列
表示具有大量数值的分类列的另一种方法是使用 categorical_column_with_hash_bucket。该特征列计算输入的一个哈希值，然后选择一个 hash_bucket_size 分桶来编码字符串。使用此列时，您不需要提供词汇表，并且可以选择使 hash_buckets 的数量远远小于实际类别的数量以节省空间。
关键点：该技术的一个重要缺点是可能存在冲突，不同的字符串被映射到同一个范围。实际上，无论如何，经过哈希处理的特征列对某些数据集都有效。
thal_hashed = feature_column.categorical_column_with_hash_bucket(
'thal', hash_bucket_size=1000)
demo(feature_column.indicator_column(thal_hashed))
WARNING:tensorflow:Layer dense_features_4 is casting an input tensor from dtype float64 to the layer's dtype of float32, which is new behavior in TensorFlow 2\. The layer has dtype float32 because its dtype defaults to floatx.
If you intended to run this layer in float32, you can safely ignore this warning. If in doubt, this warning is likely only an issue if you are porting a TensorFlow 1.X model to TensorFlow 2.
To change all layers to have dtype float64 by default, call `tf.keras.backend.set_floatx('float64')`. To change just this layer, pass dtype='float64' to the layer constructor. If you are the author of this layer, you can disable autocasting by passing autocast=False to the base Layer constructor.
[[0\. 0\. 0\. ... 0\. 0\. 0.]
[0\. 0\. 0\. ... 0\. 0\. 0.]
[0\. 0\. 0\. ... 0\. 0\. 0.]
[0\. 0\. 0\. ... 0\. 0\. 0.]
[0\. 0\. 0\. ... 0\. 0\. 0.]]
组合的特征列
将多种特征组合到一个特征中，称为特征组合（feature crosses），它让模型能够为每种特征组合学习单独的权重。此处，我们将创建一个 age 和 thal 组合的新特征。请注意，crossed_column 不会构建所有可能组合的完整列表（可能非常大）。相反，它由 hashed_column 支持，因此您可以选择表的大小。
crossed_feature = feature_column.crossed_column([age_buckets, thal], hash_bucket_size=1000)
demo(feature_column.indicator_column(crossed_feature))
WARNING:tensorflow:Layer dense_features_5 is casting an input tensor from dtype float64 to the layer's dtype of float32, which is new behavior in TensorFlow 2\. The layer has dtype float32 because its dtype defaults to floatx.
If you intended to run this layer in float32, you can safely ignore this warning. If in doubt, this warning is likely only an issue if you are porting a TensorFlow 1.X model to TensorFlow 2.
To change all layers to have dtype float64 by default, call `tf.keras.backend.set_floatx('float64')`. To change just this layer, pass dtype='float64' to the layer constructor. If you are the author of this layer, you can disable autocasting by passing autocast=False to the base Layer constructor.
[[0\. 0\. 0\. ... 0\. 0\. 0.]
[0\. 0\. 0\. ... 0\. 0\. 0.]
[0\. 0\. 0\. ... 0\. 0\. 0.]
[0\. 0\. 0\. ... 0\. 0\. 0.]
[0\. 0\. 0\. ... 0\. 0\. 0.]]
选择要使用的列
我们已经了解了如何使用几种类型的特征列。 现在我们将使用它们来训练模型。本教程的目标是向您展示使用特征列所需的完整代码（例如，机制）。我们任意地选择了几列来训练我们的模型。
关键点：如果您的目标是建立一个准确的模型，请尝试使用您自己的更大的数据集，并仔细考虑哪些特征最有意义，以及如何表示它们。
feature_columns = []
# 数值列
for header in ['age', 'trestbps', 'chol', 'thalach', 'oldpeak', 'slope', 'ca']:
feature_columns.append(feature_column.numeric_column(header))
# 分桶列
age_buckets = feature_column.bucketized_column(age, boundaries=[18, 25, 30, 35, 40, 45, 50, 55, 60, 65])
feature_columns.append(age_buckets)
# 分类列
thal = feature_column.categorical_column_with_vocabulary_list(
'thal', ['fixed', 'normal', 'reversible'])
thal_one_hot = feature_column.indicator_column(thal)
feature_columns.append(thal_one_hot)
# 嵌入列
thal_embedding = feature_column.embedding_column(thal, dimension=8)
feature_columns.append(thal_embedding)
# 组合列
crossed_feature = feature_column.crossed_column([age_buckets, thal], hash_bucket_size=1000)
crossed_feature = feature_column.indicator_column(crossed_feature)
feature_columns.append(crossed_feature)
建立一个新的特征层
现在我们已经定义了我们的特征列，我们将使用密集特征（DenseFeatures）层将特征列输入到我们的 Keras 模型中。
feature_layer = tf.keras.layers.DenseFeatures(feature_columns)
之前，我们使用一个小批量大小来演示特征列如何运转。我们将创建一个新的更大批量的输入流水线。
batch_size = 32
train_ds = df_to_dataset(train, batch_size=batch_size)
val_ds = df_to_dataset(val, shuffle=False, batch_size=batch_size)
test_ds = df_to_dataset(test, shuffle=False, batch_size=batch_size)
创建，编译和训练模型
model = tf.keras.Sequential([
feature_layer,
layers.Dense(128, activation='relu'),
layers.Dense(128, activation='relu'),
layers.Dense(1, activation='sigmoid')
])
model.compile(optimizer='adam',
loss='binary_crossentropy',
metrics=['accuracy'],
run_eagerly=True)
model.fit(train_ds,
validation_data=val_ds,
epochs=5)
Epoch 1/5
WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor, but we receive a <class 'dict'> input: {'age': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[57],
[45],
[49],
[67],
[54],
[70],
[54],
[52],
[52],
[44],
[57],
[43],
[62],
[59],
[62],
[58],
[42],
[68],
[56],
[46],
[44],
[40],
[52],
[63],
[57],
[56],
[37],
[64],
[43],
[34],
[57],
[51]])>, 'sex': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[1],
[0],
[0],
[0],
[0],
[1],
[1],
[1],
[1],
[1],
[1],
[1],
[1],
[1],
[1],
[1],
[1],
[1],
[0],
[1],
[1],
[1],
[1],
[0],
[0],
[1],
[1],
[1],
[0],
[1],
[1],
[0]])>, 'cp': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[3],
[4],
[2],
[4],
[3],
[3],
[3],
[4],
[4],
[4],
[2],
[4],
[3],
[1],
[4],
[2],
[4],
[3],
[4],
[2],
[2],
[1],
[2],
[4],
[4],
[4],
[3],
[1],
[3],
[1],
[2],
[3]])>, 'trestbps': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[128],
[138],
[134],
[106],
[110],
[160],
[150],
[112],
[125],
[112],
[154],
[150],
[130],
[170],
[120],
[120],
[136],
[118],
[200],
[101],
[120],
[140],
[120],
[124],
[128],
[125],
[130],
[110],
[122],
[118],
[124],
[140]])>, 'chol': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[229],
[236],
[271],
[223],
[214],
[269],
[232],
[230],
[212],
[290],
[232],
[247],
[231],
[288],
[267],
[284],
[315],
[277],
[288],
[197],
[263],
[199],
[325],
[197],
[303],
[249],
[250],
[211],
[213],
[182],
[261],
[308]])>, 'fbs': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[0],
[0],
[0],
[0],
[0],
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[0],
[0],
[0],
[0],
[0],
[1],
[0],
[0],
[0],
[0],
[0],
[0]])>, 'restecg': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[2],
[2],
[0],
[0],
[0],
[0],
[2],
[0],
[0],
[2],
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[0],
[2],
[2],
[0],
[2],
[0],
[2],
[0],
[2]])>, 'thalach': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[150],
[152],
[162],
[142],
[158],
[112],
[165],
[160],
[168],
[153],
[164],
[171],
[146],
[159],
[ 99],
[160],
[125],
[151],
[133],
[156],
[173],
[178],
[172],
[136],
[159],
[144],
[187],
[144],
[165],
[174],
[141],
[142]])>, 'exang': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[0],
[1],
[0],
[0],
[0],
[1],
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array([[0.4],
[0.2],
[0\. ],
[0.3],
[1.6],
[2.9],
[1.6],
[0\. ],
[1\. ],
[0\. ],
[0\. ],
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[1\. ],
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[0\. ],
[1.2],
[3.5],
[1.8],
[0.2],
[0\. ],
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[1.5]])>, 'slope': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[2],
[2],
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array([[1],
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[0],
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[1]])>, 'thal': <tf.Tensor: shape=(32, 1), dtype=string, numpy=
array([[b'reversible'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'reversible'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'reversible'],
[b'reversible'],
[b'normal'],
[b'fixed'],
[b'reversible'],
[b'reversible'],
[b'reversible'],
[b'reversible'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'normal']], dtype=object)>}
Consider rewriting this model with the Functional API.
WARNING:tensorflow:Layer dense_features_6 is casting an input tensor from dtype float64 to the layer's dtype of float32, which is new behavior in TensorFlow 2\. The layer has dtype float32 because its dtype defaults to floatx.
If you intended to run this layer in float32, you can safely ignore this warning. If in doubt, this warning is likely only an issue if you are porting a TensorFlow 1.X model to TensorFlow 2.
To change all layers to have dtype float64 by default, call `tf.keras.backend.set_floatx('float64')`. To change just this layer, pass dtype='float64' to the layer constructor. If you are the author of this layer, you can disable autocasting by passing autocast=False to the base Layer constructor.
1/7 [===>..........................] - ETA: 0s - loss: 1.9156 - accuracy: 0.8438WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor, but we receive a <class 'dict'> input: {'age': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[64],
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array([[125],
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array([[309],
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array([[0],
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array([[0],
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array([[131],
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array([[1],
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array([[1.8],
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[3\. ],
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array([[2],
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array([[0],
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array([[b'reversible'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'fixed'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'reversible'],
[b'reversible'],
[b'normal'],
[b'fixed'],
[b'reversible'],
[b'fixed'],
[b'fixed'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'fixed'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'fixed'],
[b'normal'],
[b'normal']], dtype=object)>}
Consider rewriting this model with the Functional API.
WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor, but we receive a <class 'dict'> input: {'age': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[63],
[43],
[52],
[54],
[65],
[45],
[44],
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[59],
[43],
[67],
[68],
[53],
[44],
[58],
[46],
[59],
[47],
[53]])>, 'sex': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[1],
[1],
[1],
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[0],
[1],
[1],
[0],
[1],
[1],
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[1],
[1],
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[1],
[1],
[1]])>, 'cp': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[4],
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[3],
[3],
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array([[140],
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array([[187],
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array([[0],
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array([[2],
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array([[144],
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[157],
[147],
[180],
[192],
[114],
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[174],
[142],
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[160],
[141],
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[131],
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[ 90],
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array([[1],
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[1],
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[0],
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array([[4\. ],
[2.5],
[0\. ],
[0\. ],
[0.8],
[0\. ],
[0\. ],
[0.7],
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[0\. ],
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[2\. ],
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[1.2],
[1.9],
[1.6],
[3.4],
[0.4],
[0.4],
[2.2],
[1.4],
[1\. ],
[0\. ],
[0\. ]])>, 'slope': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[1],
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[1],
[1],
[1],
[2],
[1],
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[1],
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array([[2],
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array([[b'reversible'],
[b'reversible'],
[b'reversible'],
[b'normal'],
[b'normal'],
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[b'normal'],
[b'normal'],
[b'reversible'],
[b'reversible'],
[b'reversible'],
[b'normal'],
[b'reversible'],
[b'reversible'],
[b'reversible'],
[b'reversible'],
[b'normal'],
[b'reversible'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'reversible'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'1'],
[b'normal'],
[b'reversible']], dtype=object)>}
Consider rewriting this model with the Functional API.
3/7 [===========>..................] - ETA: 0s - loss: 2.4590 - accuracy: 0.6354WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor, but we receive a <class 'dict'> input: {'age': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[71],
[61],
[41],
[48],
[54],
[51],
[43],
[50],
[52],
[57],
[59],
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[50],
[56],
[47],
[60],
[61],
[55],
[57],
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[46],
[49],
[46],
[55],
[68],
[39]])>, 'sex': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[0],
[1],
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[1],
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array([[2],
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[3],
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[4],
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array([[160],
[148],
[130],
[122],
[120],
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[100],
[150],
[120],
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[120],
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array([[302],
[203],
[214],
[222],
[188],
[245],
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[274],
[234],
[231],
[188],
[311],
[327],
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[199]])>, 'fbs': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[0],
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array([[162],
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array([[0.4],
[0\. ],
[2\. ],
[0\. ],
[1.4],
[2.4],
[0\. ],
[0\. ],
[0.8],
[1\. ],
[1.6],
[1\. ],
[1.9],
[1.2],
[0\. ],
[0\. ],
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[0\. ],
[0.6],
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[1.2],
[0.1],
[3.6],
[2\. ],
[1.8],
[3.4],
[1.5],
[0\. ]])>, 'slope': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[1],
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[1],
[1],
[1],
[2],
[1],
[2],
[2],
[2],
[1],
[1],
[1],
[2],
[2],
[2],
[1],
[2],
[1],
[3],
[2],
[1],
[2],
[2],
[2],
[2],
[2],
[1]])>, 'ca': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[2],
[1],
[0],
[0],
[1],
[0],
[0],
[0],
[1],
[3],
[0],
[0],
[2],
[0],
[0],
[0],
[0],
[1],
[1],
[1],
[0],
[2],
[0],
[0],
[1],
[1],
[0],
[3],
[2],
[0],
[0],
[0]])>, 'thal': <tf.Tensor: shape=(32, 1), dtype=string, numpy=
array([[b'normal'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'fixed'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'reversible'],
[b'reversible'],
[b'reversible'],
[b'normal'],
[b'reversible'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'normal']], dtype=object)>}
Consider rewriting this model with the Functional API.
WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor, but we receive a <class 'dict'> input: {'age': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[58],
[51],
[54],
[51],
[50],
[66],
[53],
[46],
[46],
[42],
[58],
[29],
[43],
[60],
[57],
[35],
[74],
[65],
[41],
[45],
[58],
[56],
[58],
[57],
[45],
[51],
[64],
[44],
[50],
[59],
[67],
[50]])>, 'sex': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[0],
[1],
[1],
[0],
[0],
[1],
[1],
[0],
[1],
[0],
[1],
[1],
[1],
[0],
[1],
[1],
[0],
[1],
[1],
[0],
[0],
[1],
[1],
[1],
[1],
[0],
[1],
[1],
[1],
[0],
[1],
[0]])>, 'cp': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[4],
[1],
[4],
[3],
[2],
[2],
[3],
[4],
[4],
[4],
[3],
[2],
[4],
[4],
[4],
[4],
[2],
[1],
[3],
[2],
[1],
[2],
[4],
[4],
[4],
[4],
[3],
[4],
[4],
[4],
[4],
[3]])>, 'trestbps': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[100],
[125],
[110],
[130],
[120],
[160],
[130],
[138],
[120],
[102],
[132],
[130],
[115],
[150],
[110],
[126],
[120],
[138],
[112],
[112],
[150],
[120],
[128],
[140],
[115],
[130],
[140],
[120],
[150],
[174],
[125],
[120]])>, 'chol': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[248],
[213],
[239],
[256],
[244],
[246],
[246],
[243],
[249],
[265],
[224],
[204],
[303],
[258],
[335],
[282],
[269],
[282],
[250],
[160],
[283],
[240],
[259],
[192],
[260],
[305],
[335],
[169],
[243],
[249],
[254],
[219]])>, 'fbs': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[0],
[0],
[0],
[0],
[0],
[0],
[1],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[1],
[0],
[0],
[1],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[1],
[0]])>, 'restecg': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[2],
[2],
[0],
[2],
[0],
[0],
[2],
[2],
[2],
[2],
[2],
[2],
[0],
[2],
[0],
[2],
[2],
[2],
[0],
[0],
[2],
[0],
[2],
[0],
[2],
[0],
[0],
[0],
[2],
[0],
[0],
[0]])>, 'thalach': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[122],
[125],
[126],
[149],
[162],
[120],
[173],
[152],
[144],
[122],
[173],
[202],
[181],
[157],
[143],
[156],
[121],
[174],
[179],
[138],
[162],
[169],
[130],
[148],
[185],
[142],
[158],
[144],
[128],
[143],
[163],
[158]])>, 'exang': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[0],
[1],
[1],
[0],
[0],
[1],
[0],
[1],
[0],
[0],
[0],
[0],
[0],
[0],
[1],
[1],
[1],
[0],
[0],
[0],
[0],
[0],
[1],
[0],
[0],
[1],
[0],
[1],
[0],
[1],
[0],
[0]])>, 'oldpeak': <tf.Tensor: shape=(32, 1), dtype=float64, numpy=
array([[1\. ],
[1.4],
[2.8],
[0.5],
[1.1],
[0\. ],
[0\. ],
[0\. ],
[0.8],
[0.6],
[3.2],
[0\. ],
[1.2],
[2.6],
[3\. ],
[0\. ],
[0.2],
[1.4],
[0\. ],
[0\. ],
[1\. ],
[0\. ],
[3\. ],
[0.4],
[0\. ],
[1.2],
[0\. ],
[2.8],
[2.6],
[0\. ],
[0.2],
[1.6]])>, 'slope': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[2],
[1],
[2],
[1],
[1],
[2],
[1],
[2],
[1],
[2],
[1],
[1],
[2],
[2],
[2],
[1],
[1],
[2],
[1],
[2],
[1],
[3],
[2],
[2],
[1],
[2],
[1],
[3],
[2],
[2],
[2],
[2]])>, 'ca': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[0],
[1],
[1],
[0],
[0],
[3],
[3],
[0],
[0],
[0],
[2],
[0],
[0],
[2],
[1],
[0],
[1],
[1],
[0],
[0],
[0],
[0],
[2],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[2],
[0]])>, 'thal': <tf.Tensor: shape=(32, 1), dtype=string, numpy=
array([[b'normal'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'fixed'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'reversible'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'fixed'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'fixed'],
[b'reversible'],
[b'normal'],
[b'reversible'],
[b'normal']], dtype=object)>}
Consider rewriting this model with the Functional API.
5/7 [====================>.........] - ETA: 0s - loss: 2.0828 - accuracy: 0.5938WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor, but we receive a <class 'dict'> input: {'age': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[43],
[55],
[44],
[58],
[48],
[62],
[62],
[52],
[58],
[60],
[38],
[42],
[57],
[69],
[57],
[57],
[55],
[63],
[49],
[63],
[60],
[59],
[64],
[59],
[68],
[62],
[59],
[62],
[42],
[53],
[59],
[64]])>, 'sex': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[0],
[1],
[0],
[1],
[1],
[0],
[0],
[1],
[0],
[1],
[1],
[1],
[1],
[1],
[1],
[0],
[0],
[1],
[1],
[0],
[0],
[1],
[0],
[1],
[1],
[0],
[1],
[0],
[1],
[1],
[1],
[1]])>, 'cp': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[4],
[4],
[3],
[4],
[2],
[4],
[3],
[1],
[2],
[4],
[1],
[4],
[4],
[1],
[3],
[2],
[4],
[4],
[2],
[3],
[3],
[1],
[4],
[1],
[0],
[4],
[4],
[4],
[2],
[3],
[4],
[4]])>, 'trestbps': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[132],
[132],
[118],
[150],
[110],
[140],
[130],
[118],
[136],
[145],
[120],
[140],
[132],
[160],
[150],
[130],
[128],
[130],
[130],
[135],
[102],
[134],
[180],
[178],
[144],
[150],
[164],
[140],
[120],
[130],
[138],
[128]])>, 'chol': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[341],
[353],
[242],
[270],
[229],
[394],
[263],
[186],
[319],
[282],
[231],
[226],
[207],
[234],
[126],
[236],
[205],
[330],
[266],
[252],
[318],
[204],
[325],
[270],
[193],
[244],
[176],
[268],
[295],
[197],
[271],
[263]])>, 'fbs': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[1],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[1],
[0],
[0],
[0],
[0],
[1],
[1],
[0],
[0],
[1],
[0],
[0],
[0],
[0],
[0],
[0],
[1],
[0],
[1],
[0],
[0],
[1],
[0],
[0]])>, 'restecg': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[2],
[0],
[0],
[2],
[0],
[2],
[0],
[2],
[2],
[2],
[0],
[0],
[0],
[2],
[0],
[2],
[1],
[2],
[0],
[2],
[0],
[0],
[0],
[2],
[1],
[0],
[2],
[2],
[0],
[2],
[2],
[0]])>, 'thalach': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[136],
[132],
[149],
[111],
[168],
[157],
[ 97],
[190],
[152],
[142],
[182],
[178],
[168],
[131],
[173],
[174],
[130],
[132],
[171],
[172],
[160],
[162],
[154],
[145],
[141],
[154],
[ 90],
[160],
[162],
[152],
[182],
[105]])>, 'exang': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[1],
[1],
[0],
[1],
[0],
[0],
[0],
[0],
[0],
[1],
[1],
[0],
[1],
[0],
[0],
[0],
[1],
[1],
[0],
[0],
[0],
[0],
[1],
[0],
[0],
[1],
[0],
[0],
[0],
[0],
[0],
[1]])>, 'oldpeak': <tf.Tensor: shape=(32, 1), dtype=float64, numpy=
array([[3\. ],
[1.2],
[0.3],
[0.8],
[1\. ],
[1.2],
[1.2],
[0\. ],
[0\. ],
[2.8],
[3.8],
[0\. ],
[0\. ],
[0.1],
[0.2],
[0\. ],
[2\. ],
[1.8],
[0.6],
[0\. ],
[0\. ],
[0.8],
[0\. ],
[4.2],
[3.4],
[1.4],
[1\. ],
[3.6],
[0\. ],
[1.2],
[0\. ],
[0.2]])>, 'slope': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[2],
[2],
[2],
[1],
[3],
[2],
[2],
[2],
[1],
[2],
[2],
[1],
[1],
[2],
[1],
[2],
[2],
[1],
[1],
[1],
[1],
[1],
[1],
[3],
[1],
[2],
[2],
[3],
[1],
[3],
[1],
[2]])>, 'ca': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[0],
[1],
[1],
[0],
[0],
[0],
[1],
[0],
[2],
[2],
[0],
[0],
[0],
[1],
[1],
[1],
[1],
[3],
[0],
[0],
[1],
[2],
[0],
[0],
[2],
[0],
[2],
[2],
[0],
[0],
[0],
[1]])>, 'thal': <tf.Tensor: shape=(32, 1), dtype=string, numpy=
array([[b'reversible'],
[b'reversible'],
[b'normal'],
[b'reversible'],
[b'reversible'],
[b'normal'],
[b'reversible'],
[b'fixed'],
[b'normal'],
[b'reversible'],
[b'reversible'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'reversible'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'fixed'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'reversible']], dtype=object)>}
Consider rewriting this model with the Functional API.
WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor, but we receive a <class 'dict'> input: {'age': <tf.Tensor: shape=(1, 1), dtype=int64, numpy=array([[51]])>, 'sex': <tf.Tensor: shape=(1, 1), dtype=int64, numpy=array([[1]])>, 'cp': <tf.Tensor: shape=(1, 1), dtype=int64, numpy=array([[3]])>, 'trestbps': <tf.Tensor: shape=(1, 1), dtype=int64, numpy=array([[110]])>, 'chol': <tf.Tensor: shape=(1, 1), dtype=int64, numpy=array([[175]])>, 'fbs': <tf.Tensor: shape=(1, 1), dtype=int64, numpy=array([[0]])>, 'restecg': <tf.Tensor: shape=(1, 1), dtype=int64, numpy=array([[0]])>, 'thalach': <tf.Tensor: shape=(1, 1), dtype=int64, numpy=array([[123]])>, 'exang': <tf.Tensor: shape=(1, 1), dtype=int64, numpy=array([[0]])>, 'oldpeak': <tf.Tensor: shape=(1, 1), dtype=float64, numpy=array([[0.6]])>, 'slope': <tf.Tensor: shape=(1, 1), dtype=int64, numpy=array([[1]])>, 'ca': <tf.Tensor: shape=(1, 1), dtype=int64, numpy=array([[0]])>, 'thal': <tf.Tensor: shape=(1, 1), dtype=string, numpy=array([[b'normal']], dtype=object)>}
Consider rewriting this model with the Functional API.
7/7 [==============================] - ETA: 0s - loss: 2.0670 - accuracy: 0.6062WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor, but we receive a <class 'dict'> input: {'age': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[62],
[65],
[60],
[35],
[48],
[66],
[42],
[44],
[67],
[71],
[45],
[65],
[52],
[76],
[48],
[51],
[61],
[51],
[66],
[51],
[60],
[52],
[49],
[57],
[54],
[68],
[41],
[62],
[59],
[45],
[59],
[55]])>, 'sex': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[0],
[1],
[1],
[1],
[1],
[1],
[0],
[0],
[1],
[0],
[1],
[1],
[1],
[0],
[1],
[1],
[1],
[1],
[0],
[1],
[0],
[1],
[1],
[1],
[1],
[1],
[0],
[0],
[1],
[0],
[1],
[1]])>, 'cp': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[4],
[4],
[3],
[2],
[4],
[4],
[3],
[3],
[3],
[3],
[2],
[4],
[1],
[3],
[3],
[4],
[3],
[3],
[4],
[4],
[3],
[3],
[3],
[4],
[3],
[3],
[2],
[4],
[4],
[2],
[4],
[4]])>, 'trestbps': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[124],
[120],
[140],
[122],
[124],
[112],
[120],
[108],
[152],
[110],
[128],
[110],
[152],
[140],
[124],
[140],
[150],
[100],
[178],
[140],
[120],
[172],
[118],
[110],
[120],
[180],
[105],
[138],
[140],
[130],
[135],
[160]])>, 'chol': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[209],
[177],
[185],
[192],
[274],
[212],
[209],
[141],
[212],
[265],
[308],
[248],
[298],
[197],
[255],
[299],
[243],
[222],
[228],
[261],
[178],
[199],
[149],
[201],
[258],
[274],
[198],
[294],
[177],
[234],
[234],
[289]])>, 'fbs': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[1],
[0],
[0],
[1],
[0],
[1],
[0],
[1],
[0],
[1],
[0],
[1],
[1],
[0],
[0],
[0],
[1],
[0],
[1],
[0],
[0],
[0],
[0]])>, 'restecg': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[0],
[0],
[2],
[0],
[2],
[2],
[0],
[0],
[2],
[2],
[2],
[2],
[0],
[1],
[0],
[0],
[0],
[0],
[0],
[2],
[0],
[0],
[2],
[0],
[2],
[2],
[0],
[0],
[0],
[2],
[0],
[2]])>, 'thalach': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[163],
[140],
[155],
[174],
[166],
[132],
[173],
[175],
[150],
[130],
[170],
[158],
[178],
[116],
[175],
[173],
[137],
[143],
[165],
[186],
[ 96],
[162],
[126],
[126],
[147],
[150],
[168],
[106],
[162],
[175],
[161],
[145]])>, 'exang': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[0],
[0],
[0],
[0],
[0],
[1],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[1],
[1],
[1],
[1],
[1],
[0],
[0],
[0],
[1],
[0],
[1],
[0],
[0],
[1],
[0],
[0],
[1]])>, 'oldpeak': <tf.Tensor: shape=(32, 1), dtype=float64, numpy=
array([[0\. ],
[0.4],
[3\. ],
[0\. ],
[0.5],
[0.1],
[0\. ],
[0.6],
[0.8],
[0\. ],
[0\. ],
[0.6],
[1.2],
[1.1],
[0\. ],
[1.6],
[1\. ],
[1.2],
[1\. ],
[0\. ],
[0\. ],
[0.5],
[0.8],
[1.5],
[0.4],
[1.6],
[0\. ],
[1.9],
[0\. ],
[0.6],
[0.5],
[0.8]])>, 'slope': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[1],
[1],
[2],
[1],
[2],
[1],
[2],
[2],
[2],
[1],
[1],
[1],
[2],
[2],
[1],
[1],
[2],
[2],
[2],
[1],
[1],
[1],
[1],
[2],
[2],
[2],
[1],
[2],
[1],
[2],
[2],
[2]])>, 'ca': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[0],
[0],
[0],
[0],
[0],
[1],
[0],
[0],
[0],
[1],
[0],
[2],
[0],
[0],
[2],
[0],
[0],
[0],
[2],
[0],
[0],
[0],
[3],
[0],
[0],
[0],
[1],
[3],
[1],
[0],
[0],
[1]])>, 'thal': <tf.Tensor: shape=(32, 1), dtype=string, numpy=
array([[b'normal'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'fixed'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'fixed'],
[b'reversible'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'reversible'],
[b'reversible']], dtype=object)>}
Consider rewriting this model with the Functional API.
WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor, but we receive a <class 'dict'> input: {'age': <tf.Tensor: shape=(17, 1), dtype=int64, numpy=
array([[77],
[39],
[60],
[41],
[56],
[51],
[59],
[41],
[60],
[64],
[64],
[70],
[62],
[58],
[58],
[67],
[35]])>, 'sex': <tf.Tensor: shape=(17, 1), dtype=int64, numpy=
array([[1],
[1],
[1],
[0],
[1],
[1],
[1],
[1],
[1],
[0],
[1],
[1],
[0],
[1],
[0],
[1],
[0]])>, 'cp': <tf.Tensor: shape=(17, 1), dtype=int64, numpy=
array([[4],
[4],
[4],
[2],
[3],
[3],
[2],
[2],
[4],
[4],
[4],
[2],
[4],
[4],
[4],
[4],
[4]])>, 'trestbps': <tf.Tensor: shape=(17, 1), dtype=int64, numpy=
array([[125],
[118],
[130],
[130],
[130],
[ 94],
[140],
[120],
[125],
[130],
[120],
[156],
[160],
[125],
[130],
[120],
[138]])>, 'chol': <tf.Tensor: shape=(17, 1), dtype=int64, numpy=
array([[304],
[219],
[206],
[204],
[256],
[227],
[221],
[157],
[258],
[303],
[246],
[245],
[164],
[300],
[197],
[229],
[183]])>, 'fbs': <tf.Tensor: shape=(17, 1), dtype=int64, numpy=
array([[0],
[0],
[0],
[0],
[1],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0]])>, 'restecg': <tf.Tensor: shape=(17, 1), dtype=int64, numpy=
array([[2],
[0],
[2],
[2],
[2],
[0],
[0],
[0],
[2],
[0],
[2],
[2],
[2],
[2],
[0],
[2],
[0]])>, 'thalach': <tf.Tensor: shape=(17, 1), dtype=int64, numpy=
array([[162],
[140],
[132],
[172],
[142],
[154],
[164],
[182],
[141],
[122],
[ 96],
[143],
[145],
[171],
[131],
[129],
[182]])>, 'exang': <tf.Tensor: shape=(17, 1), dtype=int64, numpy=
array([[1],
[0],
[1],
[0],
[1],
[1],
[1],
[0],
[1],
[0],
[1],
[0],
[0],
[0],
[0],
[1],
[0]])>, 'oldpeak': <tf.Tensor: shape=(17, 1), dtype=float64, numpy=
array([[0\. ],
[1.2],
[2.4],
[1.4],
[0.6],
[0\. ],
[0\. ],
[0\. ],
[2.8],
[2\. ],
[2.2],
[0\. ],
[6.2],
[0\. ],
[0.6],
[2.6],
[1.4]])>, 'slope': <tf.Tensor: shape=(17, 1), dtype=int64, numpy=
array([[1],
[2],
[2],
[1],
[2],
[1],
[1],
[1],
[2],
[2],
[3],
[1],
[3],
[1],
[2],
[2],
[1]])>, 'ca': <tf.Tensor: shape=(17, 1), dtype=int64, numpy=
array([[3],
[0],
[2],
[0],
[1],
[1],
[0],
[0],
[1],
[2],
[1],
[0],
[3],
[2],
[0],
[2],
[0]])>, 'thal': <tf.Tensor: shape=(17, 1), dtype=string, numpy=
array([[b'normal'],
[b'reversible'],
[b'reversible'],
[b'normal'],
[b'fixed'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'reversible'],
[b'normal'],
[b'reversible'],
[b'normal']], dtype=object)>}
Consider rewriting this model with the Functional API.
7/7 [==============================] - 0s 44ms/step - loss: 2.0670 - accuracy: 0.6062 - val_loss: 1.8843 - val_accuracy: 0.7347
Epoch 2/5
WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor, but we receive a <class 'dict'> input: {'age': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[57],
[56],
[50],
[52],
[56],
[61],
[68],
[34],
[58],
[50],
[40],
[51],
[47],
[35],
[45],
[64],
[44],
[44],
[38],
[57],
[58],
[51],
[59],
[43],
[41],
[55],
[47],
[56],
[54],
[43],
[64],
[57]])>, 'sex': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[1],
[0],
[0],
[1],
[1],
[1],
[0],
[1],
[0],
[1],
[1],
[1],
[1],
[1],
[0],
[0],
[1],
[1],
[1],
[1],
[1],
[0],
[0],
[1],
[1],
[0],
[1],
[1],
[1],
[1],
[1],
[1]])>, 'cp': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[4],
[4],
[3],
[1],
[4],
[4],
[3],
[1],
[1],
[3],
[1],
[3],
[3],
[4],
[2],
[4],
[4],
[2],
[1],
[3],
[3],
[3],
[4],
[4],
[3],
[4],
[4],
[4],
[4],
[4],
[4],
[4]])>, 'trestbps': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[140],
[134],
[120],
[118],
[130],
[138],
[120],
[118],
[150],
[129],
[140],
[125],
[108],
[126],
[112],
[180],
[112],
[130],
[120],
[150],
[132],
[130],
[174],
[115],
[130],
[180],
[112],
[125],
[120],
[110],
[145],
[110]])>, 'chol': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[192],
[409],
[219],
[186],
[283],
[166],
[211],
[182],
[283],
[196],
[199],
[245],
[243],
[282],
[160],
[325],
[290],
[219],
[231],
[168],
[224],
[256],
[249],
[303],
[214],
[327],
[204],
[249],
[188],
[211],
[212],
[335]])>, 'fbs': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[0],
[0],
[0],
[0],
[1],
[0],
[0],
[0],
[1],
[0],
[0],
[1],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[1],
[0],
[0],
[0],
[0]])>, 'restecg': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[0],
[2],
[0],
[2],
[2],
[2],
[2],
[2],
[2],
[0],
[0],
[2],
[0],
[2],
[0],
[0],
[2],
[2],
[0],
[0],
[2],
[2],
[0],
[0],
[2],
[1],
[0],
[2],
[0],
[0],
[2],
[0]])>, 'thalach': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[148],
[150],
[158],
[190],
[103],
[125],
[115],
[174],
[162],
[163],
[178],
[166],
[152],
[156],
[138],
[154],
[153],
[188],
[182],
[174],
[173],
[149],
[143],
[181],
[168],
[117],
[143],
[144],
[113],
[161],
[132],
[143]])>, 'exang': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[0],
[1],
[0],
[0],
[1],
[1],
[0],
[0],
[0],
[0],
[1],
[0],
[0],
[1],
[0],
[1],
[0],
[0],
[1],
[0],
[0],
[0],
[1],
[0],
[0],
[1],
[0],
[1],
[0],
[0],
[0],
[1]])>, 'oldpeak': <tf.Tensor: shape=(32, 1), dtype=float64, numpy=
array([[0.4],
[1.9],
[1.6],
[0\. ],
[1.6],
[3.6],
[1.5],
[0\. ],
[1\. ],
[0\. ],
[1.4],
[2.4],
[0\. ],
[0\. ],
[0\. ],
[0\. ],
[0\. ],
[0\. ],
[3.8],
[1.6],
[3.2],
[0.5],
[0\. ],
[1.2],
[2\. ],
[3.4],
[0.1],
[1.2],
[1.4],
[0\. ],
[2\. ],
[3\. ]])>, 'slope': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[2],
[2],
[2],
[2],
[3],
[2],
[2],
[1],
[1],
[1],
[1],
[2],
[1],
[1],
[2],
[1],
[1],
[1],
[2],
[1],
[1],
[1],
[2],
[2],
[2],
[2],
[1],
[2],
[2],
[1],
[2],
[2]])>, 'ca': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[0],
[2],
[0],
[0],
[0],
[1],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
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[1],
[0],
[0],
[0],
[2],
[0],
[0],
[0],
[0],
[0],
[0],
[1],
[1],
[0],
[2],
[1]])>, 'thal': <tf.Tensor: shape=(32, 1), dtype=string, numpy=
array([[b'fixed'],
[b'reversible'],
[b'normal'],
[b'fixed'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'reversible'],
[b'fixed'],
[b'reversible']], dtype=object)>}
Consider rewriting this model with the Functional API.
1/7 [===>..........................] - ETA: 0s - loss: 2.3728 - accuracy: 0.6875WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor, but we receive a <class 'dict'> input: {'age': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[53],
[43],
[49],
[37],
[60],
[58],
[41],
[45],
[40],
[59],
[63],
[60],
[42],
[45],
[49],
[57],
[67],
[50],
[58],
[50],
[51],
[68],
[44],
[59],
[55],
[52],
[54],
[44],
[43],
[54],
[58],
[41]])>, 'sex': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[1],
[1],
[0],
[0],
[1],
[1],
[1],
[1],
[1],
[1],
[1],
[1],
[1],
[1],
[1],
[1],
[1],
[1],
[1],
[0],
[1],
[1],
[1],
[1],
[1],
[1],
[1],
[0],
[1],
[1],
[1],
[0]])>, 'cp': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[4],
[4],
[4],
[3],
[4],
[4],
[3],
[1],
[4],
[3],
[4],
[4],
[4],
[4],
[2],
[2],
[4],
[3],
[4],
[2],
[1],
[3],
[2],
[4],
[4],
[2],
[2],
[3],
[3],
[4],
[4],
[2]])>, 'trestbps': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[123],
[120],
[130],
[120],
[130],
[128],
[112],
[110],
[110],
[150],
[140],
[145],
[140],
[142],
[130],
[124],
[120],
[140],
[128],
[120],
[125],
[118],
[120],
[164],
[132],
[120],
[108],
[118],
[130],
[140],
[150],
[126]])>, 'chol': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[282],
[177],
[269],
[215],
[253],
[216],
[250],
[264],
[167],
[212],
[187],
[282],
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[309],
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[261],
[237],
[233],
[259],
[244],
[213],
[277],
[220],
[176],
[353],
[325],
[309],
[242],
[315],
[239],
[270],
[306]])>, 'fbs': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[1],
[0],
[0],
[0],
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[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[1],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0]])>, 'restecg': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[0],
[2],
[0],
[0],
[0],
[2],
[0],
[0],
[2],
[0],
[2],
[2],
[0],
[2],
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[0],
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[2],
[0],
[2],
[0],
[0],
[2],
[0],
[0],
[0],
[0],
[0],
[0],
[2],
[0]])>, 'thalach': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[ 95],
[120],
[163],
[170],
[144],
[131],
[179],
[132],
[114],
[157],
[144],
[142],
[178],
[147],
[171],
[141],
[ 71],
[163],
[130],
[162],
[125],
[151],
[170],
[ 90],
[132],
[172],
[156],
[149],
[162],
[160],
[111],
[163]])>, 'exang': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[1],
[1],
[0],
[0],
[1],
[1],
[0],
[0],
[1],
[0],
[1],
[1],
[0],
[1],
[0],
[0],
[0],
[0],
[1],
[0],
[1],
[0],
[0],
[0],
[1],
[0],
[0],
[0],
[0],
[0],
[1],
[0]])>, 'oldpeak': <tf.Tensor: shape=(32, 1), dtype=float64, numpy=
array([[2\. ],
[2.5],
[0\. ],
[0\. ],
[1.4],
[2.2],
[0\. ],
[1.2],
[2\. ],
[1.6],
[4\. ],
[2.8],
[0\. ],
[0\. ],
[0.6],
[0.3],
[1\. ],
[0.6],
[3\. ],
[1.1],
[1.4],
[1\. ],
[0\. ],
[1\. ],
[1.2],
[0.2],
[0\. ],
[0.3],
[1.9],
[1.2],
[0.8],
[0\. ]])>, 'slope': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[2],
[2],
[1],
[1],
[1],
[2],
[1],
[2],
[2],
[1],
[1],
[2],
[1],
[2],
[1],
[1],
[2],
[2],
[2],
[1],
[1],
[1],
[1],
[2],
[2],
[1],
[1],
[2],
[1],
[1],
[1],
[1]])>, 'ca': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[2],
[0],
[0],
[0],
[1],
[3],
[0],
[0],
[0],
[0],
[2],
[2],
[0],
[3],
[0],
[0],
[0],
[1],
[2],
[0],
[1],
[1],
[0],
[2],
[1],
[0],
[0],
[1],
[1],
[0],
[0],
[0]])>, 'thal': <tf.Tensor: shape=(32, 1), dtype=string, numpy=
array([[b'reversible'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'reversible'],
[b'normal'],
[b'reversible'],
[b'reversible'],
[b'normal'],
[b'reversible'],
[b'reversible'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'reversible'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'fixed'],
[b'reversible'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'normal']], dtype=object)>}
Consider rewriting this model with the Functional API.
WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor, but we receive a <class 'dict'> input: {'age': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[37],
[44],
[58],
[54],
[29],
[56],
[62],
[62],
[44],
[57],
[67],
[50],
[56],
[56],
[61],
[51],
[67],
[58],
[42],
[66],
[52],
[57],
[46],
[48],
[70],
[65],
[53],
[60],
[57],
[34],
[57],
[50]])>, 'sex': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[1],
[1],
[1],
[1],
[1],
[1],
[0],
[1],
[1],
[1],
[0],
[1],
[1],
[0],
[0],
[1],
[0],
[0],
[1],
[1],
[1],
[1],
[1],
[1],
[1],
[1],
[1],
[1],
[1],
[0],
[1],
[1]])>, 'cp': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[3],
[3],
[2],
[4],
[2],
[4],
[4],
[2],
[3],
[4],
[4],
[4],
[1],
[2],
[4],
[4],
[3],
[2],
[1],
[2],
[4],
[4],
[4],
[4],
[3],
[1],
[4],
[4],
[4],
[2],
[4],
[4]])>, 'trestbps': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[130],
[140],
[120],
[110],
[130],
[132],
[150],
[120],
[130],
[132],
[106],
[144],
[120],
[140],
[130],
[140],
[152],
[136],
[148],
[160],
[125],
[152],
[140],
[122],
[160],
[138],
[142],
[140],
[150],
[118],
[165],
[150]])>, 'chol': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[250],
[235],
[284],
[206],
[204],
[184],
[244],
[281],
[233],
[207],
[223],
[200],
[193],
[294],
[330],
[298],
[277],
[319],
[244],
[246],
[212],
[274],
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[269],
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[276],
[210],
[289],
[243]])>, 'fbs': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[0],
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[0],
[0],
[0],
[0],
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[0],
[0],
[0],
[0],
[1],
[0]])>, 'restecg': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[0],
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[2],
[2],
[2],
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array([[187],
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[108],
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[120],
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[112],
[174],
[111],
[170],
[112],
[192],
[124],
[128]])>, 'exang': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[0],
[0],
[0],
[1],
[0],
[1],
[1],
[0],
[1],
[1],
[0],
[1],
[0],
[0],
[0],
[1],
[0],
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[0],
[1],
[1],
[0],
[1],
[0],
[1],
[0],
[1],
[0],
[0],
[0]])>, 'oldpeak': <tf.Tensor: shape=(32, 1), dtype=float64, numpy=
array([[3.5],
[0\. ],
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[0\. ],
[0\. ],
[2.1],
[1.4],
[1.4],
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[0\. ],
[0\. ],
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[1\. ],
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[1.8],
[0\. ],
[2.9],
[1.4],
[0\. ],
[1.2],
[0.6],
[0.7],
[1\. ],
[2.6]])>, 'slope': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[3],
[1],
[2],
[2],
[1],
[2],
[2],
[2],
[1],
[1],
[1],
[2],
[2],
[2],
[1],
[2],
[1],
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[2],
[1],
[2],
[2],
[1],
[2],
[2],
[1],
[2],
[2],
[1],
[2],
[2]])>, 'ca': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[0],
[0],
[0],
[1],
[0],
[1],
[0],
[1],
[0],
[0],
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[2],
[1],
[2],
[0],
[1],
[1],
[0],
[2],
[1],
[0],
[3],
[0]])>, 'thal': <tf.Tensor: shape=(32, 1), dtype=string, numpy=
array([[b'normal'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'fixed'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'reversible'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'fixed'],
[b'reversible'],
[b'reversible'],
[b'reversible'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'reversible'],
[b'reversible'],
[b'fixed'],
[b'normal'],
[b'reversible'],
[b'reversible']], dtype=object)>}
Consider rewriting this model with the Functional API.
3/7 [===========>..................] - ETA: 0s - loss: 2.1748 - accuracy: 0.6562WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor, but we receive a <class 'dict'> input: {'age': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[56],
[58],
[60],
[54],
[46],
[67],
[67],
[53],
[46],
[49],
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[54],
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[43],
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[57],
[45],
[39],
[74],
[45],
[63],
[48],
[68],
[62],
[64],
[63],
[58]])>, 'sex': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[0],
[1],
[0],
[1],
[1],
[1],
[1],
[1],
[0],
[0],
[0],
[1],
[1],
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[1],
[1],
[0],
[0],
[0],
[1],
[0],
[1],
[1],
[0],
[1],
[0],
[1]])>, 'cp': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[4],
[4],
[3],
[4],
[3],
[4],
[4],
[3],
[3],
[2],
[3],
[4],
[4],
[4],
[4],
[1],
[4],
[4],
[3],
[3],
[2],
[4],
[3],
[2],
[4],
[3],
[2],
[0],
[4],
[3],
[4],
[3]])>, 'trestbps': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[200],
[100],
[102],
[124],
[150],
[100],
[160],
[130],
[142],
[134],
[122],
[110],
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[134],
[110],
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[130],
[150],
[154],
[138],
[ 94],
[120],
[104],
[135],
[110],
[144],
[140],
[125],
[124],
[105]])>, 'chol': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[288],
[234],
[318],
[266],
[231],
[299],
[286],
[246],
[177],
[271],
[213],
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[193],
[247],
[203],
[204],
[254],
[267],
[231],
[232],
[232],
[236],
[199],
[269],
[208],
[252],
[229],
[193],
[394],
[309],
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array([[1],
[0],
[0],
[0],
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[0],
[0],
[1],
[0],
[0],
[0],
[0]])>, 'restecg': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[2],
[0],
[0],
[2],
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[2],
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[2],
[2],
[0],
[1],
[2],
[0],
[0],
[2]])>, 'thalach': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[133],
[156],
[160],
[109],
[147],
[125],
[108],
[173],
[160],
[162],
[165],
[126],
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[171],
[161],
[162],
[159],
[ 99],
[146],
[165],
[164],
[152],
[179],
[121],
[148],
[172],
[168],
[141],
[157],
[131],
[136],
[154]])>, 'exang': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[1],
[0],
[0],
[1],
[0],
[1],
[1],
[0],
[1],
[0],
[0],
[1],
[0],
[0],
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[0],
[1],
[0],
[0],
[0],
[1],
[0],
[1],
[1],
[0],
[0],
[0],
[0],
[1],
[1],
[1]])>, 'oldpeak': <tf.Tensor: shape=(32, 1), dtype=float64, numpy=
array([[4\. ],
[0.1],
[0\. ],
[2.2],
[3.6],
[0.9],
[1.5],
[0\. ],
[1.4],
[0\. ],
[0.2],
[2.8],
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[0\. ],
[0.8],
[0\. ],
[1.8],
[1.8],
[1.6],
[0\. ],
[0.2],
[0\. ],
[0.2],
[3\. ],
[0\. ],
[1\. ],
[3.4],
[1.2],
[1.8],
[0\. ],
[0.6]])>, 'slope': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[3],
[1],
[1],
[2],
[2],
[2],
[2],
[1],
[3],
[2],
[2],
[2],
[2],
[1],
[1],
[1],
[1],
[2],
[2],
[1],
[1],
[2],
[1],
[1],
[2],
[1],
[3],
[1],
[2],
[2],
[2],
[2]])>, 'ca': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[2],
[1],
[1],
[1],
[0],
[2],
[3],
[3],
[0],
[0],
[0],
[1],
[2],
[0],
[1],
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[0],
[1],
[0],
[0],
[1],
[0],
[0],
[0],
[2],
[0],
[0],
[0],
[0]])>, 'thal': <tf.Tensor: shape=(32, 1), dtype=string, numpy=
array([[b'reversible'],
[b'reversible'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'reversible'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'reversible'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'reversible']], dtype=object)>}
Consider rewriting this model with the Functional API.
WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor, but we receive a <class 'dict'> input: {'age': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[49],
[65],
[43],
[42],
[54],
[42],
[53],
[57],
[64],
[67],
[56],
[66],
[59],
[52],
[57],
[42],
[71],
[64],
[58],
[55],
[69],
[52],
[60],
[59],
[42],
[51],
[46],
[53],
[51],
[55],
[66],
[64]])>, 'sex': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[1],
[0],
[0],
[1],
[0],
[1],
[0],
[0],
[1],
[0],
[1],
[1],
[1],
[1],
[0],
[1],
[0],
[1],
[0],
[0],
[1],
[1],
[0],
[1],
[0],
[0],
[0],
[1],
[1],
[1],
[1],
[0]])>, 'cp': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[3],
[3],
[4],
[2],
[3],
[3],
[4],
[2],
[4],
[3],
[2],
[4],
[3],
[4],
[4],
[4],
[2],
[1],
[4],
[2],
[1],
[4],
[4],
[0],
[4],
[4],
[4],
[3],
[3],
[4],
[4],
[3]])>, 'trestbps': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[120],
[140],
[132],
[120],
[110],
[120],
[130],
[130],
[128],
[115],
[120],
[120],
[126],
[128],
[120],
[136],
[160],
[170],
[100],
[132],
[160],
[112],
[150],
[164],
[102],
[130],
[138],
[130],
[110],
[140],
[160],
[140]])>, 'chol': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[188],
[417],
[341],
[295],
[214],
[240],
[264],
[236],
[263],
[564],
[240],
[302],
[218],
[255],
[354],
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[227],
[248],
[342],
[234],
[230],
[258],
[176],
[265],
[305],
[243],
[197],
[175],
[217],
[228],
[313]])>, 'fbs': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[0],
[1],
[1],
[0],
[0],
[1],
[0],
[0],
[0],
[0],
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[0],
[0],
[1],
[0],
[0],
[0],
[1],
[0],
[0],
[0],
[0]])>, 'restecg': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[0],
[2],
[2],
[0],
[0],
[0],
[2],
[2],
[0],
[2],
[0],
[2],
[0],
[0],
[0],
[0],
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[2],
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[0],
[2],
[0],
[2],
[0],
[2],
[0],
[2],
[2],
[0],
[0],
[2],
[0]])>, 'thalach': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[139],
[157],
[136],
[162],
[158],
[194],
[143],
[174],
[105],
[160],
[169],
[151],
[134],
[161],
[163],
[125],
[162],
[155],
[122],
[166],
[131],
[160],
[157],
[ 90],
[122],
[142],
[152],
[152],
[123],
[111],
[138],
[133]])>, 'exang': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[0],
[0],
[1],
[0],
[0],
[0],
[0],
[0],
[1],
[0],
[0],
[0],
[0],
[1],
[1],
[1],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[1],
[1],
[0],
[0],
[1],
[0],
[0]])>, 'oldpeak': <tf.Tensor: shape=(32, 1), dtype=float64, numpy=
array([[2\. ],
[0.8],
[3\. ],
[0\. ],
[1.6],
[0.8],
[0.4],
[0\. ],
[0.2],
[1.6],
[0\. ],
[0.4],
[2.2],
[0\. ],
[0.6],
[1.8],
[0.4],
[0.6],
[1\. ],
[1.2],
[0.1],
[0\. ],
[2.6],
[1\. ],
[0.6],
[1.2],
[0\. ],
[1.2],
[0.6],
[5.6],
[2.3],
[0.2]])>, 'slope': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[2],
[1],
[2],
[1],
[2],
[3],
[2],
[2],
[2],
[2],
[3],
[2],
[2],
[1],
[1],
[2],
[1],
[2],
[2],
[1],
[2],
[1],
[2],
[1],
[2],
[2],
[2],
[3],
[1],
[3],
[1],
[1]])>, 'ca': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[3],
[1],
[0],
[0],
[0],
[0],
[0],
[1],
[1],
[0],
[0],
[0],
[1],
[1],
[0],
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[1],
[1],
[2],
[2],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0]])>, 'thal': <tf.Tensor: shape=(32, 1), dtype=string, numpy=
array([[b'reversible'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'fixed'],
[b'reversible'],
[b'normal'],
[b'fixed'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'1'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'fixed'],
[b'reversible']], dtype=object)>}
Consider rewriting this model with the Functional API.
5/7 [====================>.........] - ETA: 0s - loss: 1.5868 - accuracy: 0.6313WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor, but we receive a <class 'dict'> input: {'age': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[46],
[47],
[57],
[64],
[41],
[62],
[62],
[57],
[59],
[54],
[60],
[58],
[70],
[46],
[57],
[41],
[64],
[63],
[59],
[52],
[42],
[59],
[55],
[62],
[59],
[51],
[63],
[44],
[47],
[45],
[67],
[44]])>, 'sex': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[1],
[1],
[1],
[1],
[1],
[0],
[1],
[1],
[1],
[1],
[1],
[1],
[1],
[1],
[0],
[1],
[1],
[1],
[1],
[1],
[1],
[1],
[0],
[0],
[1],
[0],
[1],
[1],
[1],
[1],
[1],
[1]])>, 'cp': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[2],
[3],
[3],
[3],
[2],
[4],
[2],
[3],
[4],
[4],
[4],
[4],
[4],
[4],
[4],
[2],
[1],
[4],
[1],
[2],
[3],
[4],
[4],
[3],
[1],
[3],
[4],
[4],
[3],
[4],
[4],
[2]])>, 'trestbps': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[101],
[130],
[150],
[140],
[135],
[140],
[128],
[128],
[110],
[122],
[117],
[114],
[130],
[120],
[128],
[110],
[110],
[130],
[170],
[134],
[130],
[138],
[128],
[130],
[178],
[140],
[130],
[120],
[138],
[115],
[125],
[120]])>, 'chol': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[197],
[253],
[126],
[335],
[203],
[268],
[208],
[229],
[239],
[286],
[230],
[318],
[322],
[249],
[303],
[235],
[211],
[254],
[288],
[201],
[180],
[271],
[205],
[263],
[270],
[308],
[330],
[169],
[257],
[260],
[254],
[263]])>, 'fbs': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[1],
[0],
[1],
[0],
[0],
[0],
[1],
[0],
[0],
[0],
[1],
[0],
[0],
[0],
[0],
[0],
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[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[1],
[0],
[0],
[0],
[1],
[0]])>, 'restecg': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[0],
[0],
[0],
[0],
[0],
[2],
[2],
[2],
[2],
[2],
[0],
[1],
[2],
[2],
[2],
[0],
[2],
[2],
[2],
[0],
[0],
[2],
[1],
[0],
[2],
[2],
[2],
[0],
[2],
[2],
[0],
[0]])>, 'thalach': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[156],
[179],
[173],
[158],
[132],
[160],
[140],
[150],
[142],
[116],
[160],
[140],
[109],
[144],
[159],
[153],
[144],
[147],
[159],
[158],
[150],
[182],
[130],
[ 97],
[145],
[142],
[132],
[144],
[156],
[185],
[163],
[173]])>, 'exang': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[1],
[1],
[1],
[0],
[0],
[0],
[0],
[0],
[1],
[0],
[0],
[0],
[0],
[0],
[1],
[0],
[0],
[0],
[1],
[1],
[0],
[0],
[0],
[0]])>, 'oldpeak': <tf.Tensor: shape=(32, 1), dtype=float64, numpy=
array([[0\. ],
[0\. ],
[0.2],
[0\. ],
[0\. ],
[3.6],
[0\. ],
[0.4],
[1.2],
[3.2],
[1.4],
[4.4],
[2.4],
[0.8],
[0\. ],
[0\. ],
[1.8],
[1.4],
[0.2],
[0.8],
[0\. ],
[0\. ],
[2\. ],
[1.2],
[4.2],
[1.5],
[1.8],
[2.8],
[0\. ],
[0\. ],
[0.2],
[0\. ]])>, 'slope': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[1],
[1],
[1],
[1],
[2],
[3],
[1],
[2],
[2],
[2],
[1],
[3],
[2],
[1],
[1],
[1],
[2],
[2],
[2],
[1],
[1],
[1],
[2],
[2],
[3],
[1],
[1],
[3],
[1],
[1],
[2],
[1]])>, 'ca': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[0],
[0],
[1],
[0],
[0],
[2],
[0],
[1],
[1],
[2],
[2],
[3],
[3],
[0],
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[1],
[0],
[1],
[0],
[0],
[1],
[1],
[0],
[1],
[3],
[0],
[0],
[0],
[2],
[0]])>, 'thal': <tf.Tensor: shape=(32, 1), dtype=string, numpy=
array([[b'reversible'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'fixed'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'reversible'],
[b'normal'],
[b'reversible'],
[b'fixed'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'reversible'],
[b'reversible'],
[b'normal'],
[b'reversible'],
[b'fixed'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'reversible']], dtype=object)>}
Consider rewriting this model with the Functional API.
WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor, but we receive a <class 'dict'> input: {'age': <tf.Tensor: shape=(1, 1), dtype=int64, numpy=array([[54]])>, 'sex': <tf.Tensor: shape=(1, 1), dtype=int64, numpy=array([[0]])>, 'cp': <tf.Tensor: shape=(1, 1), dtype=int64, numpy=array([[3]])>, 'trestbps': <tf.Tensor: shape=(1, 1), dtype=int64, numpy=array([[135]])>, 'chol': <tf.Tensor: shape=(1, 1), dtype=int64, numpy=array([[304]])>, 'fbs': <tf.Tensor: shape=(1, 1), dtype=int64, numpy=array([[1]])>, 'restecg': <tf.Tensor: shape=(1, 1), dtype=int64, numpy=array([[0]])>, 'thalach': <tf.Tensor: shape=(1, 1), dtype=int64, numpy=array([[170]])>, 'exang': <tf.Tensor: shape=(1, 1), dtype=int64, numpy=array([[0]])>, 'oldpeak': <tf.Tensor: shape=(1, 1), dtype=float64, numpy=array([[0.]])>, 'slope': <tf.Tensor: shape=(1, 1), dtype=int64, numpy=array([[1]])>, 'ca': <tf.Tensor: shape=(1, 1), dtype=int64, numpy=array([[0]])>, 'thal': <tf.Tensor: shape=(1, 1), dtype=string, numpy=array([[b'normal']], dtype=object)>}
Consider rewriting this model with the Functional API.
7/7 [==============================] - ETA: 0s - loss: 1.5046 - accuracy: 0.5803WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor, but we receive a <class 'dict'> input: {'age': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[62],
[65],
[60],
[35],
[48],
[66],
[42],
[44],
[67],
[71],
[45],
[65],
[52],
[76],
[48],
[51],
[61],
[51],
[66],
[51],
[60],
[52],
[49],
[57],
[54],
[68],
[41],
[62],
[59],
[45],
[59],
[55]])>, 'sex': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[0],
[1],
[1],
[1],
[1],
[1],
[0],
[0],
[1],
[0],
[1],
[1],
[1],
[0],
[1],
[1],
[1],
[1],
[0],
[1],
[0],
[1],
[1],
[1],
[1],
[1],
[0],
[0],
[1],
[0],
[1],
[1]])>, 'cp': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[4],
[4],
[3],
[2],
[4],
[4],
[3],
[3],
[3],
[3],
[2],
[4],
[1],
[3],
[3],
[4],
[3],
[3],
[4],
[4],
[3],
[3],
[3],
[4],
[3],
[3],
[2],
[4],
[4],
[2],
[4],
[4]])>, 'trestbps': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[124],
[120],
[140],
[122],
[124],
[112],
[120],
[108],
[152],
[110],
[128],
[110],
[152],
[140],
[124],
[140],
[150],
[100],
[178],
[140],
[120],
[172],
[118],
[110],
[120],
[180],
[105],
[138],
[140],
[130],
[135],
[160]])>, 'chol': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[209],
[177],
[185],
[192],
[274],
[212],
[209],
[141],
[212],
[265],
[308],
[248],
[298],
[197],
[255],
[299],
[243],
[222],
[228],
[261],
[178],
[199],
[149],
[201],
[258],
[274],
[198],
[294],
[177],
[234],
[234],
[289]])>, 'fbs': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[1],
[0],
[0],
[1],
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[1],
[0],
[1],
[0],
[1],
[0],
[1],
[1],
[0],
[0],
[0],
[1],
[0],
[1],
[0],
[0],
[0],
[0]])>, 'restecg': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[0],
[0],
[2],
[0],
[2],
[2],
[0],
[0],
[2],
[2],
[2],
[2],
[0],
[1],
[0],
[0],
[0],
[0],
[0],
[2],
[0],
[0],
[2],
[0],
[2],
[2],
[0],
[0],
[0],
[2],
[0],
[2]])>, 'thalach': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[163],
[140],
[155],
[174],
[166],
[132],
[173],
[175],
[150],
[130],
[170],
[158],
[178],
[116],
[175],
[173],
[137],
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[ 96],
[162],
[126],
[126],
[147],
[150],
[168],
[106],
[162],
[175],
[161],
[145]])>, 'exang': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[0],
[0],
[0],
[0],
[0],
[1],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[1],
[1],
[1],
[1],
[1],
[0],
[0],
[0],
[1],
[0],
[1],
[0],
[0],
[1],
[0],
[0],
[1]])>, 'oldpeak': <tf.Tensor: shape=(32, 1), dtype=float64, numpy=
array([[0\. ],
[0.4],
[3\. ],
[0\. ],
[0.5],
[0.1],
[0\. ],
[0.6],
[0.8],
[0\. ],
[0\. ],
[0.6],
[1.2],
[1.1],
[0\. ],
[1.6],
[1\. ],
[1.2],
[1\. ],
[0\. ],
[0\. ],
[0.5],
[0.8],
[1.5],
[0.4],
[1.6],
[0\. ],
[1.9],
[0\. ],
[0.6],
[0.5],
[0.8]])>, 'slope': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[1],
[1],
[2],
[1],
[2],
[1],
[2],
[2],
[2],
[1],
[1],
[1],
[2],
[2],
[1],
[1],
[2],
[2],
[2],
[1],
[1],
[1],
[1],
[2],
[2],
[2],
[1],
[2],
[1],
[2],
[2],
[2]])>, 'ca': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[0],
[0],
[0],
[0],
[0],
[1],
[0],
[0],
[0],
[1],
[0],
[2],
[0],
[0],
[2],
[0],
[0],
[0],
[2],
[0],
[0],
[0],
[3],
[0],
[0],
[0],
[1],
[3],
[1],
[0],
[0],
[1]])>, 'thal': <tf.Tensor: shape=(32, 1), dtype=string, numpy=
array([[b'normal'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'fixed'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'fixed'],
[b'reversible'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'reversible'],
[b'reversible']], dtype=object)>}
Consider rewriting this model with the Functional API.
WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor, but we receive a <class 'dict'> input: {'age': <tf.Tensor: shape=(17, 1), dtype=int64, numpy=
array([[77],
[39],
[60],
[41],
[56],
[51],
[59],
[41],
[60],
[64],
[64],
[70],
[62],
[58],
[58],
[67],
[35]])>, 'sex': <tf.Tensor: shape=(17, 1), dtype=int64, numpy=
array([[1],
[1],
[1],
[0],
[1],
[1],
[1],
[1],
[1],
[0],
[1],
[1],
[0],
[1],
[0],
[1],
[0]])>, 'cp': <tf.Tensor: shape=(17, 1), dtype=int64, numpy=
array([[4],
[4],
[4],
[2],
[3],
[3],
[2],
[2],
[4],
[4],
[4],
[2],
[4],
[4],
[4],
[4],
[4]])>, 'trestbps': <tf.Tensor: shape=(17, 1), dtype=int64, numpy=
array([[125],
[118],
[130],
[130],
[130],
[ 94],
[140],
[120],
[125],
[130],
[120],
[156],
[160],
[125],
[130],
[120],
[138]])>, 'chol': <tf.Tensor: shape=(17, 1), dtype=int64, numpy=
array([[304],
[219],
[206],
[204],
[256],
[227],
[221],
[157],
[258],
[303],
[246],
[245],
[164],
[300],
[197],
[229],
[183]])>, 'fbs': <tf.Tensor: shape=(17, 1), dtype=int64, numpy=
array([[0],
[0],
[0],
[0],
[1],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0]])>, 'restecg': <tf.Tensor: shape=(17, 1), dtype=int64, numpy=
array([[2],
[0],
[2],
[2],
[2],
[0],
[0],
[0],
[2],
[0],
[2],
[2],
[2],
[2],
[0],
[2],
[0]])>, 'thalach': <tf.Tensor: shape=(17, 1), dtype=int64, numpy=
array([[162],
[140],
[132],
[172],
[142],
[154],
[164],
[182],
[141],
[122],
[ 96],
[143],
[145],
[171],
[131],
[129],
[182]])>, 'exang': <tf.Tensor: shape=(17, 1), dtype=int64, numpy=
array([[1],
[0],
[1],
[0],
[1],
[1],
[1],
[0],
[1],
[0],
[1],
[0],
[0],
[0],
[0],
[1],
[0]])>, 'oldpeak': <tf.Tensor: shape=(17, 1), dtype=float64, numpy=
array([[0\. ],
[1.2],
[2.4],
[1.4],
[0.6],
[0\. ],
[0\. ],
[0\. ],
[2.8],
[2\. ],
[2.2],
[0\. ],
[6.2],
[0\. ],
[0.6],
[2.6],
[1.4]])>, 'slope': <tf.Tensor: shape=(17, 1), dtype=int64, numpy=
array([[1],
[2],
[2],
[1],
[2],
[1],
[1],
[1],
[2],
[2],
[3],
[1],
[3],
[1],
[2],
[2],
[1]])>, 'ca': <tf.Tensor: shape=(17, 1), dtype=int64, numpy=
array([[3],
[0],
[2],
[0],
[1],
[1],
[0],
[0],
[1],
[2],
[1],
[0],
[3],
[2],
[0],
[2],
[0]])>, 'thal': <tf.Tensor: shape=(17, 1), dtype=string, numpy=
array([[b'normal'],
[b'reversible'],
[b'reversible'],
[b'normal'],
[b'fixed'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'reversible'],
[b'normal'],
[b'reversible'],
[b'normal']], dtype=object)>}
Consider rewriting this model with the Functional API.
7/7 [==============================] - 0s 42ms/step - loss: 1.5046 - accuracy: 0.5803 - val_loss: 0.5387 - val_accuracy: 0.7551
Epoch 3/5
WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor, but we receive a <class 'dict'> input: {'age': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[49],
[43],
[58],
[42],
[42],
[59],
[62],
[64],
[56],
[59],
[60],
[67],
[58],
[43],
[54],
[52],
[63],
[37],
[44],
[34],
[49],
[63],
[50],
[64],
[56],
[57],
[68],
[42],
[47],
[62],
[53],
[35]])>, 'sex': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[1],
[1],
[1],
[0],
[1],
[1],
[1],
[1],
[1],
[0],
[1],
[0],
[0],
[1],
[1],
[1],
[1],
[1],
[1],
[1],
[0],
[0],
[1],
[1],
[0],
[1],
[1],
[1],
[1],
[0],
[1],
[1]])>, 'cp': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[3],
[4],
[4],
[4],
[3],
[1],
[2],
[1],
[4],
[4],
[4],
[4],
[4],
[3],
[4],
[2],
[4],
[3],
[2],
[1],
[2],
[4],
[3],
[4],
[4],
[4],
[4],
[2],
[4],
[3],
[3],
[4]])>, 'trestbps': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[120],
[150],
[100],
[102],
[120],
[134],
[120],
[110],
[132],
[174],
[130],
[106],
[100],
[130],
[120],
[120],
[130],
[130],
[120],
[118],
[134],
[124],
[140],
[128],
[200],
[132],
[144],
[120],
[112],
[130],
[130],
[126]])>, 'chol': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[188],
[247],
[234],
[265],
[240],
[204],
[281],
[211],
[184],
[249],
[253],
[223],
[248],
[315],
[188],
[325],
[254],
[250],
[220],
[182],
[271],
[197],
[233],
[263],
[288],
[207],
[193],
[295],
[204],
[263],
[197],
[282]])>, 'fbs': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[0],
[0],
[0],
[0],
[1],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
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[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[1],
[0],
[1],
[0],
[0],
[0],
[1],
[0]])>, 'restecg': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[0],
[0],
[0],
[2],
[0],
[0],
[2],
[2],
[2],
[0],
[0],
[0],
[2],
[0],
[0],
[0],
[2],
[0],
[0],
[2],
[0],
[0],
[0],
[0],
[2],
[0],
[0],
[0],
[0],
[0],
[2],
[2]])>, 'thalach': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[139],
[171],
[156],
[122],
[194],
[162],
[103],
[144],
[105],
[143],
[144],
[142],
[122],
[162],
[113],
[172],
[147],
[187],
[170],
[174],
[162],
[136],
[163],
[105],
[133],
[168],
[141],
[162],
[143],
[ 97],
[152],
[156]])>, 'exang': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[0],
[0],
[0],
[0],
[0],
[0],
[0],
[1],
[1],
[1],
[1],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[1],
[0],
[1],
[1],
[1],
[0],
[0],
[0],
[0],
[0],
[1]])>, 'oldpeak': <tf.Tensor: shape=(32, 1), dtype=float64, numpy=
array([[2\. ],
[1.5],
[0.1],
[0.6],
[0.8],
[0.8],
[1.4],
[1.8],
[2.1],
[0\. ],
[1.4],
[0.3],
[1\. ],
[1.9],
[1.4],
[0.2],
[1.4],
[3.5],
[0\. ],
[0\. ],
[0\. ],
[0\. ],
[0.6],
[0.2],
[4\. ],
[0\. ],
[3.4],
[0\. ],
[0.1],
[1.2],
[1.2],
[0\. ]])>, 'slope': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[2],
[1],
[1],
[2],
[3],
[1],
[2],
[2],
[2],
[2],
[1],
[1],
[2],
[1],
[2],
[1],
[2],
[3],
[1],
[1],
[2],
[2],
[2],
[2],
[3],
[1],
[2],
[1],
[1],
[2],
[3],
[1]])>, 'ca': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[3],
[0],
[1],
[0],
[0],
[2],
[1],
[0],
[1],
[0],
[1],
[2],
[0],
[1],
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[0],
[0],
[1],
[1],
[2],
[0],
[2],
[0],
[0],
[1],
[0],
[0]])>, 'thal': <tf.Tensor: shape=(32, 1), dtype=string, numpy=
array([[b'reversible'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'fixed'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'reversible'],
[b'reversible'],
[b'reversible'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'reversible']], dtype=object)>}
Consider rewriting this model with the Functional API.
1/7 [===>..........................] - ETA: 0s - loss: 0.4726 - accuracy: 0.7500WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor, but we receive a <class 'dict'> input: {'age': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[38],
[55],
[61],
[53],
[47],
[52],
[59],
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[52],
[54],
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[45],
[66],
[60],
[64],
[68],
[69],
[67],
[51],
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array([[1],
[1],
[1],
[1],
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[1],
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[1],
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[1],
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[1],
[0],
[1],
[0],
[1],
[1],
[1],
[1],
[0],
[1],
[1],
[1]])>, 'cp': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[1],
[4],
[4],
[4],
[3],
[2],
[0],
[3],
[2],
[4],
[4],
[2],
[3],
[2],
[4],
[4],
[4],
[4],
[4],
[4],
[1],
[4],
[4],
[3],
[4],
[0],
[1],
[4],
[4],
[2],
[3],
[3]])>, 'trestbps': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[120],
[132],
[148],
[142],
[108],
[134],
[164],
[140],
[140],
[112],
[122],
[120],
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[120],
[110],
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[100],
[138],
[110],
[138],
[120],
[102],
[145],
[144],
[160],
[120],
[130],
[120],
[150],
[130]])>, 'chol': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[231],
[353],
[203],
[226],
[243],
[201],
[176],
[235],
[294],
[230],
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[246],
[319],
[192],
[354],
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[211],
[299],
[271],
[264],
[236],
[302],
[318],
[212],
[193],
[234],
[237],
[305],
[284],
[168],
[180]])>, 'fbs': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[0],
[0],
[0],
[0],
[0],
[0],
[1],
[0],
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[0],
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[0],
[1],
[1],
[0],
[0],
[0],
[0],
[0]])>, 'restecg': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[0],
[0],
[0],
[2],
[0],
[0],
[0],
[2],
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[0],
[2],
[2],
[0],
[2],
[1],
[2],
[0],
[0],
[2],
[0],
[0]])>, 'thalach': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[182],
[132],
[161],
[111],
[152],
[158],
[ 90],
[180],
[153],
[160],
[116],
[121],
[173],
[152],
[148],
[163],
[159],
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[125],
[182],
[132],
[152],
[151],
[160],
[132],
[141],
[131],
[ 71],
[142],
[160],
[174],
[150]])>, 'exang': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[1],
[1],
[0],
[1],
[0],
[0],
[0],
[0],
[0],
[0],
[1],
[1],
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[0],
[1],
[0],
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[1],
[0],
[0],
[1],
[0],
[0],
[0],
[0],
[0],
[0],
[1],
[0],
[0],
[0]])>, 'oldpeak': <tf.Tensor: shape=(32, 1), dtype=float64, numpy=
array([[3.8],
[1.2],
[0\. ],
[0\. ],
[0\. ],
[0.8],
[1\. ],
[0\. ],
[1.3],
[0\. ],
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[0\. ],
[0\. ],
[0.9],
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[0.4],
[0\. ],
[2\. ],
[3.4],
[0.1],
[1\. ],
[1.2],
[1.8],
[1.6],
[0\. ]])>, 'slope': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[2],
[2],
[1],
[1],
[1],
[1],
[1],
[1],
[2],
[1],
[2],
[1],
[1],
[1],
[2],
[1],
[1],
[1],
[2],
[1],
[2],
[2],
[2],
[1],
[2],
[1],
[2],
[2],
[2],
[2],
[1],
[1]])>, 'ca': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[0],
[1],
[1],
[0],
[0],
[1],
[2],
[0],
[0],
[1],
[2],
[1],
[3],
[2],
[0],
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[0],
[2],
[0],
[0],
[0],
[0],
[1],
[2],
[2],
[1],
[0],
[0],
[0],
[0],
[0]])>, 'thal': <tf.Tensor: shape=(32, 1), dtype=string, numpy=
array([[b'reversible'],
[b'reversible'],
[b'reversible'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'1'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'fixed'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'fixed'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'normal']], dtype=object)>}
Consider rewriting this model with the Functional API.
WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor, but we receive a <class 'dict'> input: {'age': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[59],
[63],
[62],
[48],
[40],
[50],
[29],
[67],
[51],
[45],
[65],
[41],
[45],
[46],
[43],
[57],
[64],
[55],
[41],
[41],
[61],
[57],
[52],
[49],
[66],
[58],
[59],
[62],
[63],
[62],
[58],
[66]])>, 'sex': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[1],
[1],
[1],
[1],
[1],
[1],
[1],
[1],
[0],
[1],
[1],
[1],
[1],
[1],
[1],
[1],
[1],
[0],
[1],
[1],
[0],
[1],
[1],
[1],
[1],
[1],
[1],
[1],
[0],
[0],
[1],
[1]])>, 'cp': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[4],
[4],
[4],
[2],
[4],
[4],
[2],
[4],
[3],
[4],
[1],
[2],
[4],
[2],
[4],
[3],
[1],
[4],
[3],
[2],
[4],
[3],
[1],
[2],
[4],
[4],
[3],
[3],
[3],
[4],
[3],
[2]])>, 'trestbps': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[164],
[140],
[120],
[110],
[110],
[150],
[130],
[125],
[140],
[142],
[138],
[135],
[115],
[101],
[115],
[150],
[170],
[128],
[130],
[110],
[130],
[128],
[118],
[130],
[160],
[128],
[126],
[130],
[135],
[140],
[132],
[160]])>, 'chol': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[176],
[187],
[267],
[229],
[167],
[243],
[204],
[254],
[308],
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[282],
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[197],
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[126],
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[330],
[229],
[186],
[266],
[228],
[259],
[218],
[231],
[252],
[268],
[224],
[246]])>, 'fbs': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[1],
[0],
[0],
[0],
[0],
[0],
[0],
[1],
[0],
[0],
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[0],
[0],
[0],
[0],
[0],
[1],
[0],
[0],
[0],
[0],
[0]])>, 'restecg': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[2],
[2],
[0],
[0],
[2],
[2],
[2],
[0],
[2],
[2],
[2],
[0],
[2],
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[2],
[2],
[0],
[2],
[2],
[0],
[0],
[2],
[2],
[2],
[0]])>, 'thalach': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[ 90],
[144],
[ 99],
[168],
[114],
[128],
[202],
[163],
[142],
[147],
[174],
[132],
[185],
[156],
[181],
[173],
[155],
[130],
[168],
[153],
[169],
[150],
[190],
[171],
[138],
[130],
[134],
[146],
[172],
[160],
[173],
[120]])>, 'exang': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[0],
[1],
[1],
[0],
[1],
[0],
[0],
[0],
[0],
[1],
[0],
[0],
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[0],
[0],
[0],
[0],
[0],
[1],
[0],
[0],
[0],
[0],
[0],
[1]])>, 'oldpeak': <tf.Tensor: shape=(32, 1), dtype=float64, numpy=
array([[1\. ],
[4\. ],
[1.8],
[1\. ],
[2\. ],
[2.6],
[0\. ],
[0.2],
[1.5],
[0\. ],
[1.4],
[0\. ],
[0\. ],
[0\. ],
[1.2],
[0.2],
[0.6],
[2\. ],
[2\. ],
[0\. ],
[0\. ],
[0.4],
[0\. ],
[0.6],
[2.3],
[3\. ],
[2.2],
[1.8],
[0\. ],
[3.6],
[3.2],
[0\. ]])>, 'slope': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[2],
[1],
[2],
[3],
[2],
[2],
[1],
[2],
[1],
[2],
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[1],
[2],
[2],
[1],
[1],
[2],
[2],
[2],
[1],
[3],
[1],
[2]])>, 'ca': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[2],
[2],
[2],
[0],
[0],
[0],
[0],
[2],
[1],
[3],
[1],
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[1],
[3],
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[2],
[2],
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array([[b'fixed'],
[b'reversible'],
[b'reversible'],
[b'reversible'],
[b'reversible'],
[b'reversible'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'fixed'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'reversible'],
[b'reversible'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'fixed'],
[b'normal'],
[b'fixed'],
[b'reversible'],
[b'fixed'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'fixed']], dtype=object)>}
Consider rewriting this model with the Functional API.
3/7 [===========>..................] - ETA: 0s - loss: 0.9738 - accuracy: 0.6979WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor, but we receive a <class 'dict'> input: {'age': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[58],
[60],
[58],
[44],
[59],
[57],
[53],
[54],
[54],
[45],
[64],
[57],
[60],
[46],
[68],
[47],
[64],
[40],
[62],
[42],
[44],
[42],
[43],
[39],
[61],
[44],
[50],
[54],
[49],
[50],
[68],
[46]])>, 'sex': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[1],
[1],
[0],
[1],
[1],
[1],
[0],
[1],
[1],
[0],
[0],
[0],
[1],
[0],
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[1],
[0],
[1],
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[1],
[1],
[1],
[0],
[0],
[1],
[0],
[0],
[1],
[0],
[1],
[1],
[1]])>, 'cp': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[4],
[4],
[1],
[2],
[1],
[4],
[4],
[2],
[4],
[2],
[3],
[2],
[4],
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[3],
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[4],
[1],
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[3],
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[3],
[2],
[4],
[4],
[3],
[3],
[3]])>, 'trestbps': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[114],
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[150],
[120],
[170],
[110],
[130],
[108],
[140],
[112],
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[112],
[148],
[132],
[ 94],
[138],
[118],
[120],
[124],
[130],
[129],
[118],
[150]])>, 'chol': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[318],
[293],
[283],
[263],
[288],
[335],
[264],
[309],
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[282],
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[211],
[253],
[325],
[199],
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[290],
[244],
[341],
[199],
[166],
[242],
[244],
[266],
[269],
[196],
[277],
[231]])>, 'fbs': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[0],
[0],
[1],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
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[0],
[1],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0]])>, 'restecg': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[1],
[2],
[2],
[0],
[2],
[0],
[2],
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[0],
[0],
[0],
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[2],
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[0],
[2],
[0],
[0],
[2],
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[0],
[0],
[0]])>, 'thalach': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[140],
[170],
[162],
[173],
[159],
[143],
[143],
[156],
[160],
[138],
[133],
[174],
[142],
[152],
[115],
[179],
[154],
[178],
[140],
[125],
[153],
[178],
[136],
[179],
[125],
[149],
[162],
[109],
[163],
[163],
[151],
[147]])>, 'exang': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[0],
[0],
[0],
[0],
[0],
[1],
[0],
[0],
[0],
[0],
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[0],
[0],
[1],
[0],
[1],
[0],
[0],
[1],
[0],
[0],
[0],
[0]])>, 'oldpeak': <tf.Tensor: shape=(32, 1), dtype=float64, numpy=
array([[4.4],
[1.2],
[1\. ],
[0\. ],
[0.2],
[3\. ],
[0.4],
[0\. ],
[1.2],
[0\. ],
[0.2],
[0\. ],
[2.8],
[0\. ],
[1.5],
[0\. ],
[0\. ],
[1.4],
[0\. ],
[1.8],
[0\. ],
[0.8],
[3\. ],
[0\. ],
[3.6],
[0.3],
[1.1],
[2.2],
[0\. ],
[0\. ],
[1\. ],
[3.6]])>, 'slope': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[3],
[2],
[1],
[1],
[2],
[2],
[2],
[1],
[1],
[2],
[1],
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[1],
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[1],
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array([[3],
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[1],
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[1],
[0],
[0],
[1],
[0]])>, 'thal': <tf.Tensor: shape=(32, 1), dtype=string, numpy=
array([[b'fixed'],
[b'reversible'],
[b'normal'],
[b'reversible'],
[b'reversible'],
[b'reversible'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'fixed'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'normal']], dtype=object)>}
Consider rewriting this model with the Functional API.
WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor, but we receive a <class 'dict'> input: {'age': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[41],
[62],
[45],
[71],
[42],
[47],
[51],
[54],
[59],
[67],
[57],
[56],
[58],
[54],
[67],
[63],
[37],
[34],
[43],
[65],
[46],
[44],
[60],
[70],
[52],
[64],
[56],
[55],
[50],
[51],
[70],
[55]])>, 'sex': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[1],
[0],
[1],
[0],
[1],
[1],
[1],
[0],
[1],
[0],
[1],
[1],
[1],
[1],
[0],
[1],
[0],
[0],
[1],
[0],
[1],
[1],
[1],
[1],
[1],
[1],
[1],
[0],
[0],
[1],
[1],
[0]])>, 'cp': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[3],
[4],
[4],
[2],
[4],
[3],
[3],
[3],
[3],
[3],
[2],
[4],
[4],
[3],
[3],
[4],
[3],
[2],
[4],
[3],
[4],
[4],
[4],
[3],
[4],
[3],
[1],
[4],
[3],
[3],
[4],
[2]])>, 'trestbps': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[112],
[150],
[104],
[160],
[140],
[138],
[110],
[135],
[150],
[152],
[124],
[130],
[128],
[150],
[115],
[130],
[120],
[118],
[120],
[140],
[120],
[120],
[117],
[160],
[125],
[125],
[120],
[180],
[120],
[125],
[130],
[132]])>, 'chol': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[250],
[244],
[208],
[302],
[226],
[257],
[175],
[304],
[212],
[277],
[261],
[283],
[216],
[232],
[564],
[330],
[215],
[210],
[177],
[417],
[249],
[169],
[230],
[269],
[212],
[309],
[193],
[327],
[219],
[245],
[322],
[342]])>, 'fbs': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[0],
[0],
[0],
[0],
[0],
[0],
[0],
[1],
[1],
[0],
[0],
[1],
[0],
[0],
[0],
[1],
[0],
[0],
[0],
[1],
[0],
[0],
[1],
[0],
[0],
[0],
[0],
[0],
[0],
[1],
[0],
[0]])>, 'restecg': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[0],
[0],
[2],
[0],
[0],
[2],
[0],
[0],
[0],
[0],
[0],
[2],
[2],
[2],
[2],
[2],
[0],
[0],
[2],
[2],
[2],
[0],
[0],
[0],
[0],
[0],
[2],
[1],
[0],
[2],
[2],
[0]])>, 'thalach': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[179],
[154],
[148],
[162],
[178],
[156],
[123],
[170],
[157],
[172],
[141],
[103],
[131],
[165],
[160],
[132],
[170],
[192],
[120],
[157],
[144],
[144],
[160],
[112],
[168],
[131],
[162],
[117],
[158],
[166],
[109],
[166]])>, 'exang': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[0],
[1],
[1],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[1],
[1],
[0],
[0],
[1],
[0],
[0],
[1],
[0],
[0],
[1],
[1],
[1],
[0],
[1],
[0],
[1],
[0],
[0],
[0],
[0]])>, 'oldpeak': <tf.Tensor: shape=(32, 1), dtype=float64, numpy=
array([[0\. ],
[1.4],
[3\. ],
[0.4],
[0\. ],
[0\. ],
[0.6],
[0\. ],
[1.6],
[0\. ],
[0.3],
[1.6],
[2.2],
[1.6],
[1.6],
[1.8],
[0\. ],
[0.7],
[2.5],
[0.8],
[0.8],
[2.8],
[1.4],
[2.9],
[1\. ],
[1.8],
[1.9],
[3.4],
[1.6],
[2.4],
[2.4],
[1.2]])>, 'slope': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[1],
[2],
[2],
[1],
[1],
[1],
[1],
[1],
[1],
[1],
[1],
[3],
[2],
[1],
[2],
[1],
[1],
[1],
[2],
[1],
[1],
[3],
[1],
[2],
[1],
[2],
[2],
[2],
[2],
[2],
[2],
[1]])>, 'ca': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[0],
[0],
[0],
[2],
[0],
[0],
[0],
[0],
[0],
[1],
[0],
[0],
[3],
[0],
[0],
[3],
[0],
[0],
[0],
[1],
[0],
[0],
[2],
[1],
[2],
[0],
[0],
[0],
[0],
[0],
[3],
[0]])>, 'thal': <tf.Tensor: shape=(32, 1), dtype=string, numpy=
array([[b'normal'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'reversible'],
[b'reversible'],
[b'reversible'],
[b'reversible'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'reversible'],
[b'fixed'],
[b'reversible'],
[b'reversible'],
[b'reversible'],
[b'reversible'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'normal']], dtype=object)>}
Consider rewriting this model with the Functional API.
5/7 [====================>.........] - ETA: 0s - loss: 1.0254 - accuracy: 0.7188WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor, but we receive a <class 'dict'> input: {'age': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[54],
[59],
[58],
[44],
[43],
[56],
[55],
[57],
[54],
[54],
[57],
[57],
[57],
[48],
[64],
[51],
[52],
[51],
[56],
[50],
[46],
[56],
[59],
[60],
[51],
[67],
[53],
[44],
[46],
[41],
[58],
[57]])>, 'sex': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[1],
[1],
[1],
[1],
[0],
[1],
[1],
[1],
[0],
[1],
[1],
[1],
[1],
[1],
[1],
[1],
[1],
[1],
[1],
[1],
[0],
[0],
[1],
[0],
[0],
[1],
[1],
[1],
[1],
[0],
[1],
[0]])>, 'cp': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[4],
[1],
[4],
[2],
[3],
[4],
[4],
[4],
[3],
[4],
[2],
[4],
[4],
[4],
[3],
[4],
[4],
[1],
[2],
[4],
[3],
[4],
[4],
[4],
[3],
[4],
[4],
[3],
[4],
[2],
[3],
[4]])>, 'trestbps': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[110],
[178],
[150],
[130],
[122],
[125],
[140],
[165],
[110],
[110],
[154],
[150],
[152],
[122],
[140],
[140],
[128],
[125],
[120],
[144],
[142],
[134],
[110],
[150],
[130],
[160],
[123],
[130],
[140],
[126],
[105],
[128]])>, 'chol': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[239],
[270],
[270],
[219],
[213],
[249],
[217],
[289],
[214],
[206],
[232],
[276],
[274],
[222],
[335],
[298],
[255],
[213],
[240],
[200],
[177],
[409],
[239],
[258],
[256],
[286],
[282],
[233],
[311],
[306],
[240],
[303]])>, 'fbs': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[0],
[0],
[0],
[0],
[0],
[1],
[0],
[1],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0]])>, 'restecg': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[0],
[2],
[2],
[2],
[0],
[2],
[0],
[2],
[0],
[2],
[2],
[2],
[0],
[2],
[0],
[0],
[0],
[2],
[0],
[2],
[2],
[2],
[2],
[2],
[2],
[2],
[0],
[0],
[0],
[0],
[2],
[2]])>, 'thalach': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[126],
[145],
[111],
[188],
[165],
[144],
[111],
[124],
[158],
[108],
[164],
[112],
[ 88],
[186],
[158],
[122],
[161],
[125],
[169],
[126],
[160],
[150],
[142],
[157],
[149],
[108],
[ 95],
[179],
[120],
[163],
[154],
[159]])>, 'exang': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[1],
[0],
[1],
[0],
[0],
[1],
[1],
[0],
[0],
[1],
[0],
[1],
[1],
[0],
[0],
[1],
[1],
[1],
[0],
[1],
[1],
[1],
[1],
[0],
[0],
[1],
[1],
[1],
[1],
[0],
[1],
[0]])>, 'oldpeak': <tf.Tensor: shape=(32, 1), dtype=float64, numpy=
array([[2.8],
[4.2],
[0.8],
[0\. ],
[0.2],
[1.2],
[5.6],
[1\. ],
[1.6],
[0\. ],
[0\. ],
[0.6],
[1.2],
[0\. ],
[0\. ],
[4.2],
[0\. ],
[1.4],
[0\. ],
[0.9],
[1.4],
[1.9],
[1.2],
[2.6],
[0.5],
[1.5],
[2\. ],
[0.4],
[1.8],
[0\. ],
[0.6],
[0\. ]])>, 'slope': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[2],
[3],
[1],
[1],
[2],
[2],
[3],
[2],
[2],
[2],
[1],
[2],
[2],
[1],
[1],
[2],
[1],
[1],
[3],
[2],
[3],
[2],
[2],
[2],
[1],
[2],
[2],
[1],
[2],
[1],
[2],
[1]])>, 'ca': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[1],
[0],
[0],
[0],
[0],
[1],
[0],
[3],
[0],
[1],
[1],
[1],
[1],
[0],
[0],
[3],
[1],
[1],
[0],
[0],
[0],
[2],
[1],
[2],
[0],
[3],
[2],
[0],
[2],
[0],
[0],
[1]])>, 'thal': <tf.Tensor: shape=(32, 1), dtype=string, numpy=
array([[b'reversible'],
[b'reversible'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'fixed'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'reversible'],
[b'reversible'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'reversible'],
[b'normal']], dtype=object)>}
Consider rewriting this model with the Functional API.
WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor, but we receive a <class 'dict'> input: {'age': <tf.Tensor: shape=(1, 1), dtype=int64, numpy=array([[62]])>, 'sex': <tf.Tensor: shape=(1, 1), dtype=int64, numpy=array([[0]])>, 'cp': <tf.Tensor: shape=(1, 1), dtype=int64, numpy=array([[4]])>, 'trestbps': <tf.Tensor: shape=(1, 1), dtype=int64, numpy=array([[140]])>, 'chol': <tf.Tensor: shape=(1, 1), dtype=int64, numpy=array([[394]])>, 'fbs': <tf.Tensor: shape=(1, 1), dtype=int64, numpy=array([[0]])>, 'restecg': <tf.Tensor: shape=(1, 1), dtype=int64, numpy=array([[2]])>, 'thalach': <tf.Tensor: shape=(1, 1), dtype=int64, numpy=array([[157]])>, 'exang': <tf.Tensor: shape=(1, 1), dtype=int64, numpy=array([[0]])>, 'oldpeak': <tf.Tensor: shape=(1, 1), dtype=float64, numpy=array([[1.2]])>, 'slope': <tf.Tensor: shape=(1, 1), dtype=int64, numpy=array([[2]])>, 'ca': <tf.Tensor: shape=(1, 1), dtype=int64, numpy=array([[0]])>, 'thal': <tf.Tensor: shape=(1, 1), dtype=string, numpy=array([[b'normal']], dtype=object)>}
Consider rewriting this model with the Functional API.
7/7 [==============================] - ETA: 0s - loss: 1.0386 - accuracy: 0.6995WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor, but we receive a <class 'dict'> input: {'age': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[62],
[65],
[60],
[35],
[48],
[66],
[42],
[44],
[67],
[71],
[45],
[65],
[52],
[76],
[48],
[51],
[61],
[51],
[66],
[51],
[60],
[52],
[49],
[57],
[54],
[68],
[41],
[62],
[59],
[45],
[59],
[55]])>, 'sex': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[0],
[1],
[1],
[1],
[1],
[1],
[0],
[0],
[1],
[0],
[1],
[1],
[1],
[0],
[1],
[1],
[1],
[1],
[0],
[1],
[0],
[1],
[1],
[1],
[1],
[1],
[0],
[0],
[1],
[0],
[1],
[1]])>, 'cp': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[4],
[4],
[3],
[2],
[4],
[4],
[3],
[3],
[3],
[3],
[2],
[4],
[1],
[3],
[3],
[4],
[3],
[3],
[4],
[4],
[3],
[3],
[3],
[4],
[3],
[3],
[2],
[4],
[4],
[2],
[4],
[4]])>, 'trestbps': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[124],
[120],
[140],
[122],
[124],
[112],
[120],
[108],
[152],
[110],
[128],
[110],
[152],
[140],
[124],
[140],
[150],
[100],
[178],
[140],
[120],
[172],
[118],
[110],
[120],
[180],
[105],
[138],
[140],
[130],
[135],
[160]])>, 'chol': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[209],
[177],
[185],
[192],
[274],
[212],
[209],
[141],
[212],
[265],
[308],
[248],
[298],
[197],
[255],
[299],
[243],
[222],
[228],
[261],
[178],
[199],
[149],
[201],
[258],
[274],
[198],
[294],
[177],
[234],
[234],
[289]])>, 'fbs': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[1],
[0],
[0],
[1],
[0],
[1],
[0],
[1],
[0],
[1],
[0],
[1],
[1],
[0],
[0],
[0],
[1],
[0],
[1],
[0],
[0],
[0],
[0]])>, 'restecg': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[0],
[0],
[2],
[0],
[2],
[2],
[0],
[0],
[2],
[2],
[2],
[2],
[0],
[1],
[0],
[0],
[0],
[0],
[0],
[2],
[0],
[0],
[2],
[0],
[2],
[2],
[0],
[0],
[0],
[2],
[0],
[2]])>, 'thalach': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[163],
[140],
[155],
[174],
[166],
[132],
[173],
[175],
[150],
[130],
[170],
[158],
[178],
[116],
[175],
[173],
[137],
[143],
[165],
[186],
[ 96],
[162],
[126],
[126],
[147],
[150],
[168],
[106],
[162],
[175],
[161],
[145]])>, 'exang': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[0],
[0],
[0],
[0],
[0],
[1],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[1],
[1],
[1],
[1],
[1],
[0],
[0],
[0],
[1],
[0],
[1],
[0],
[0],
[1],
[0],
[0],
[1]])>, 'oldpeak': <tf.Tensor: shape=(32, 1), dtype=float64, numpy=
array([[0\. ],
[0.4],
[3\. ],
[0\. ],
[0.5],
[0.1],
[0\. ],
[0.6],
[0.8],
[0\. ],
[0\. ],
[0.6],
[1.2],
[1.1],
[0\. ],
[1.6],
[1\. ],
[1.2],
[1\. ],
[0\. ],
[0\. ],
[0.5],
[0.8],
[1.5],
[0.4],
[1.6],
[0\. ],
[1.9],
[0\. ],
[0.6],
[0.5],
[0.8]])>, 'slope': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[1],
[1],
[2],
[1],
[2],
[1],
[2],
[2],
[2],
[1],
[1],
[1],
[2],
[2],
[1],
[1],
[2],
[2],
[2],
[1],
[1],
[1],
[1],
[2],
[2],
[2],
[1],
[2],
[1],
[2],
[2],
[2]])>, 'ca': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[0],
[0],
[0],
[0],
[0],
[1],
[0],
[0],
[0],
[1],
[0],
[2],
[0],
[0],
[2],
[0],
[0],
[0],
[2],
[0],
[0],
[0],
[3],
[0],
[0],
[0],
[1],
[3],
[1],
[0],
[0],
[1]])>, 'thal': <tf.Tensor: shape=(32, 1), dtype=string, numpy=
array([[b'normal'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'fixed'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'fixed'],
[b'reversible'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'reversible'],
[b'reversible']], dtype=object)>}
Consider rewriting this model with the Functional API.
WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor, but we receive a <class 'dict'> input: {'age': <tf.Tensor: shape=(17, 1), dtype=int64, numpy=
array([[77],
[39],
[60],
[41],
[56],
[51],
[59],
[41],
[60],
[64],
[64],
[70],
[62],
[58],
[58],
[67],
[35]])>, 'sex': <tf.Tensor: shape=(17, 1), dtype=int64, numpy=
array([[1],
[1],
[1],
[0],
[1],
[1],
[1],
[1],
[1],
[0],
[1],
[1],
[0],
[1],
[0],
[1],
[0]])>, 'cp': <tf.Tensor: shape=(17, 1), dtype=int64, numpy=
array([[4],
[4],
[4],
[2],
[3],
[3],
[2],
[2],
[4],
[4],
[4],
[2],
[4],
[4],
[4],
[4],
[4]])>, 'trestbps': <tf.Tensor: shape=(17, 1), dtype=int64, numpy=
array([[125],
[118],
[130],
[130],
[130],
[ 94],
[140],
[120],
[125],
[130],
[120],
[156],
[160],
[125],
[130],
[120],
[138]])>, 'chol': <tf.Tensor: shape=(17, 1), dtype=int64, numpy=
array([[304],
[219],
[206],
[204],
[256],
[227],
[221],
[157],
[258],
[303],
[246],
[245],
[164],
[300],
[197],
[229],
[183]])>, 'fbs': <tf.Tensor: shape=(17, 1), dtype=int64, numpy=
array([[0],
[0],
[0],
[0],
[1],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0]])>, 'restecg': <tf.Tensor: shape=(17, 1), dtype=int64, numpy=
array([[2],
[0],
[2],
[2],
[2],
[0],
[0],
[0],
[2],
[0],
[2],
[2],
[2],
[2],
[0],
[2],
[0]])>, 'thalach': <tf.Tensor: shape=(17, 1), dtype=int64, numpy=
array([[162],
[140],
[132],
[172],
[142],
[154],
[164],
[182],
[141],
[122],
[ 96],
[143],
[145],
[171],
[131],
[129],
[182]])>, 'exang': <tf.Tensor: shape=(17, 1), dtype=int64, numpy=
array([[1],
[0],
[1],
[0],
[1],
[1],
[1],
[0],
[1],
[0],
[1],
[0],
[0],
[0],
[0],
[1],
[0]])>, 'oldpeak': <tf.Tensor: shape=(17, 1), dtype=float64, numpy=
array([[0\. ],
[1.2],
[2.4],
[1.4],
[0.6],
[0\. ],
[0\. ],
[0\. ],
[2.8],
[2\. ],
[2.2],
[0\. ],
[6.2],
[0\. ],
[0.6],
[2.6],
[1.4]])>, 'slope': <tf.Tensor: shape=(17, 1), dtype=int64, numpy=
array([[1],
[2],
[2],
[1],
[2],
[1],
[1],
[1],
[2],
[2],
[3],
[1],
[3],
[1],
[2],
[2],
[1]])>, 'ca': <tf.Tensor: shape=(17, 1), dtype=int64, numpy=
array([[3],
[0],
[2],
[0],
[1],
[1],
[0],
[0],
[1],
[2],
[1],
[0],
[3],
[2],
[0],
[2],
[0]])>, 'thal': <tf.Tensor: shape=(17, 1), dtype=string, numpy=
array([[b'normal'],
[b'reversible'],
[b'reversible'],
[b'normal'],
[b'fixed'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'reversible'],
[b'normal'],
[b'reversible'],
[b'normal']], dtype=object)>}
Consider rewriting this model with the Functional API.
7/7 [==============================] - 0s 42ms/step - loss: 1.0386 - accuracy: 0.6995 - val_loss: 0.6039 - val_accuracy: 0.6531
Epoch 4/5
WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor, but we receive a <class 'dict'> input: {'age': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[52],
[58],
[49],
[60],
[34],
[41],
[58],
[58],
[59],
[52],
[49],
[57],
[42],
[57],
[67],
[44],
[62],
[53],
[50],
[48],
[58],
[58],
[53],
[42],
[56],
[45],
[60],
[62],
[44],
[57],
[65],
[46]])>, 'sex': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[1],
[0],
[0],
[1],
[1],
[1],
[1],
[1],
[0],
[1],
[1],
[1],
[1],
[1],
[1],
[0],
[1],
[1],
[0],
[1],
[1],
[0],
[0],
[0],
[1],
[1],
[0],
[0],
[1],
[1],
[1],
[1]])>, 'cp': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[4],
[2],
[4],
[4],
[1],
[3],
[2],
[4],
[4],
[4],
[2],
[4],
[3],
[3],
[4],
[3],
[2],
[3],
[2],
[2],
[4],
[4],
[4],
[4],
[2],
[1],
[4],
[4],
[2],
[3],
[1],
[3]])>, 'trestbps': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[125],
[136],
[130],
[140],
[118],
[112],
[120],
[128],
[174],
[112],
[130],
[152],
[120],
[150],
[100],
[118],
[128],
[130],
[120],
[110],
[150],
[100],
[130],
[102],
[120],
[110],
[150],
[140],
[130],
[150],
[138],
[150]])>, 'chol': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[212],
[319],
[269],
[293],
[182],
[250],
[284],
[259],
[249],
[230],
[266],
[274],
[240],
[126],
[299],
[242],
[208],
[197],
[244],
[229],
[270],
[248],
[264],
[265],
[240],
[264],
[258],
[394],
[219],
[168],
[282],
[231]])>, 'fbs': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[0],
[1],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[1],
[1],
[0],
[0],
[1],
[1],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[1],
[0]])>, 'restecg': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[0],
[2],
[0],
[2],
[2],
[0],
[2],
[2],
[0],
[0],
[0],
[0],
[0],
[0],
[2],
[0],
[2],
[2],
[0],
[0],
[2],
[2],
[2],
[2],
[0],
[0],
[2],
[2],
[2],
[0],
[2],
[0]])>, 'thalach': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[168],
[152],
[163],
[170],
[174],
[179],
[160],
[130],
[143],
[160],
[171],
[ 88],
[194],
[173],
[125],
[149],
[140],
[152],
[162],
[168],
[111],
[122],
[143],
[122],
[169],
[132],
[157],
[157],
[188],
[174],
[174],
[147]])>, 'exang': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[0],
[0],
[0],
[0],
[0],
[0],
[0],
[1],
[1],
[0],
[0],
[1],
[0],
[0],
[1],
[0],
[0],
[0],
[0],
[0],
[1],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0]])>, 'oldpeak': <tf.Tensor: shape=(32, 1), dtype=float64, numpy=
array([[1\. ],
[0\. ],
[0\. ],
[1.2],
[0\. ],
[0\. ],
[1.8],
[3\. ],
[0\. ],
[0\. ],
[0.6],
[1.2],
[0.8],
[0.2],
[0.9],
[0.3],
[0\. ],
[1.2],
[1.1],
[1\. ],
[0.8],
[1\. ],
[0.4],
[0.6],
[0\. ],
[1.2],
[2.6],
[1.2],
[0\. ],
[1.6],
[1.4],
[3.6]])>, 'slope': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[1],
[1],
[1],
[2],
[1],
[1],
[2],
[2],
[2],
[1],
[1],
[2],
[3],
[1],
[2],
[2],
[1],
[3],
[1],
[3],
[1],
[2],
[2],
[2],
[3],
[2],
[2],
[2],
[1],
[1],
[2],
[2]])>, 'ca': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[2],
[2],
[0],
[2],
[0],
[0],
[0],
[2],
[0],
[1],
[0],
[1],
[0],
[1],
[2],
[1],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[2],
[0],
[0],
[0],
[1],
[0]])>, 'thal': <tf.Tensor: shape=(32, 1), dtype=string, numpy=
array([[b'reversible'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'reversible'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'normal']], dtype=object)>}
Consider rewriting this model with the Functional API.
1/7 [===>..........................] - ETA: 0s - loss: 0.5663 - accuracy: 0.6875WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor, but we receive a <class 'dict'> input: {'age': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[63],
[57],
[44],
[41],
[46],
[66],
[51],
[46],
[62],
[58],
[41],
[68],
[58],
[64],
[60],
[63],
[45],
[57],
[52],
[59],
[29],
[42],
[63],
[56],
[58],
[40],
[55],
[67],
[52],
[64],
[43],
[42]])>, 'sex': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[0],
[1],
[1],
[1],
[0],
[1],
[1],
[1],
[0],
[1],
[0],
[1],
[1],
[1],
[0],
[1],
[1],
[0],
[1],
[1],
[1],
[1],
[1],
[1],
[1],
[1],
[0],
[1],
[1],
[1],
[1],
[1]])>, 'cp': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[3],
[2],
[3],
[2],
[4],
[4],
[4],
[4],
[4],
[4],
[2],
[0],
[3],
[3],
[3],
[4],
[4],
[4],
[4],
[1],
[2],
[1],
[4],
[4],
[4],
[4],
[4],
[4],
[2],
[1],
[4],
[4]])>, 'trestbps': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[135],
[154],
[140],
[135],
[138],
[160],
[140],
[120],
[140],
[100],
[126],
[144],
[132],
[125],
[102],
[130],
[115],
[128],
[128],
[134],
[130],
[148],
[130],
[130],
[114],
[110],
[180],
[160],
[120],
[170],
[110],
[140]])>, 'chol': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[252],
[232],
[235],
[203],
[243],
[228],
[298],
[249],
[268],
[234],
[306],
[193],
[224],
[309],
[318],
[254],
[260],
[303],
[255],
[204],
[204],
[244],
[330],
[283],
[318],
[167],
[327],
[286],
[325],
[227],
[211],
[226]])>, 'fbs': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[1],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[1],
[1],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0]])>, 'restecg': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[2],
[2],
[2],
[0],
[2],
[2],
[0],
[2],
[2],
[0],
[0],
[1],
[2],
[0],
[0],
[2],
[2],
[2],
[0],
[0],
[2],
[2],
[2],
[2],
[1],
[2],
[1],
[2],
[0],
[2],
[0],
[0]])>, 'thalach': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[172],
[164],
[180],
[132],
[152],
[138],
[122],
[144],
[160],
[156],
[163],
[141],
[173],
[131],
[160],
[147],
[185],
[159],
[161],
[162],
[202],
[178],
[132],
[103],
[140],
[114],
[117],
[108],
[172],
[155],
[161],
[178]])>, 'exang': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[0],
[0],
[0],
[0],
[1],
[0],
[1],
[0],
[0],
[0],
[0],
[0],
[0],
[1],
[0],
[0],
[0],
[0],
[1],
[0],
[0],
[0],
[1],
[1],
[0],
[1],
[1],
[1],
[0],
[0],
[0],
[0]])>, 'oldpeak': <tf.Tensor: shape=(32, 1), dtype=float64, numpy=
array([[0\. ],
[0\. ],
[0\. ],
[0\. ],
[0\. ],
[2.3],
[4.2],
[0.8],
[3.6],
[0.1],
[0\. ],
[3.4],
[3.2],
[1.8],
[0\. ],
[1.4],
[0\. ],
[0\. ],
[0\. ],
[0.8],
[0\. ],
[0.8],
[1.8],
[1.6],
[4.4],
[2\. ],
[3.4],
[1.5],
[0.2],
[0.6],
[0\. ],
[0\. ]])>, 'slope': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[1],
[1],
[1],
[2],
[2],
[1],
[2],
[1],
[3],
[1],
[1],
[1],
[1],
[2],
[1],
[2],
[1],
[1],
[1],
[1],
[1],
[1],
[1],
[3],
[3],
[2],
[2],
[2],
[1],
[2],
[1],
[1]])>, 'ca': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[0],
[1],
[0],
[0],
[0],
[0],
[3],
[0],
[2],
[1],
[0],
[2],
[2],
[0],
[1],
[1],
[0],
[1],
[1],
[2],
[0],
[2],
[3],
[0],
[3],
[0],
[0],
[3],
[0],
[0],
[0],
[0]])>, 'thal': <tf.Tensor: shape=(32, 1), dtype=string, numpy=
array([[b'normal'],
[b'normal'],
[b'normal'],
[b'fixed'],
[b'normal'],
[b'fixed'],
[b'reversible'],
[b'reversible'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'reversible'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'reversible'],
[b'fixed'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'reversible'],
[b'normal']], dtype=object)>}
Consider rewriting this model with the Functional API.
WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor, but we receive a <class 'dict'> input: {'age': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[59],
[66],
[55],
[43],
[56],
[45],
[64],
[57],
[57],
[67],
[44],
[54],
[41],
[63],
[54],
[49],
[46],
[62],
[68],
[56],
[64],
[59],
[54],
[43],
[56],
[69],
[59],
[58],
[54],
[64],
[39],
[61]])>, 'sex': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[1],
[1],
[0],
[1],
[0],
[1],
[1],
[0],
[1],
[0],
[1],
[0],
[1],
[1],
[1],
[0],
[0],
[0],
[1],
[0],
[0],
[1],
[1],
[1],
[1],
[1],
[1],
[0],
[1],
[1],
[0],
[0]])>, 'cp': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[0],
[2],
[2],
[4],
[4],
[4],
[1],
[2],
[2],
[3],
[3],
[3],
[3],
[4],
[4],
[2],
[3],
[4],
[4],
[4],
[3],
[3],
[4],
[3],
[4],
[1],
[1],
[1],
[4],
[4],
[3],
[4]])>, 'trestbps': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[164],
[160],
[132],
[115],
[200],
[142],
[110],
[130],
[124],
[115],
[130],
[135],
[130],
[140],
[120],
[134],
[142],
[150],
[144],
[134],
[140],
[126],
[122],
[130],
[125],
[160],
[170],
[150],
[110],
[145],
[ 94],
[130]])>, 'chol': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[176],
[246],
[342],
[303],
[288],
[309],
[211],
[236],
[261],
[564],
[233],
[304],
[214],
[187],
[188],
[271],
[177],
[244],
[193],
[409],
[313],
[218],
[286],
[315],
[249],
[234],
[288],
[283],
[206],
[212],
[199],
[330]])>, 'fbs': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[1],
[0],
[0],
[0],
[1],
[0],
[0],
[0],
[0],
[0],
[0],
[1],
[0],
[0],
[0],
[0],
[0],
[0],
[1],
[0],
[0],
[1],
[0],
[0],
[1],
[1],
[0],
[1],
[0],
[0],
[0],
[0]])>, 'restecg': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[0],
[0],
[0],
[0],
[2],
[2],
[2],
[2],
[0],
[2],
[0],
[0],
[2],
[2],
[0],
[0],
[2],
[0],
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[2],
[0],
[0],
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[2],
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[2],
[2],
[2],
[2],
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[2]])>, 'thalach': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[ 90],
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[132],
[179],
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array([[0],
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[0],
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[1],
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[0],
[0],
[0],
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[0],
[1],
[0],
[1],
[0],
[0],
[0],
[1],
[0],
[0],
[0]])>, 'oldpeak': <tf.Tensor: shape=(32, 1), dtype=float64, numpy=
array([[1\. ],
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[1.2],
[0.1],
[0.2],
[1\. ],
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array([[1],
[2],
[1],
[2],
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[2],
[2],
[2],
[1],
[2],
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[1],
[2],
[2],
[1],
[2],
[2],
[2],
[1],
[2],
[2],
[1],
[1]])>, 'ca': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[2],
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[1],
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[0],
[0],
[1],
[2],
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[0]])>, 'thal': <tf.Tensor: shape=(32, 1), dtype=string, numpy=
array([[b'1'],
[b'fixed'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'reversible'],
[b'reversible'],
[b'fixed'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'fixed'],
[b'normal'],
[b'normal']], dtype=object)>}
Consider rewriting this model with the Functional API.
3/7 [===========>..................] - ETA: 0s - loss: 0.6564 - accuracy: 0.6562WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor, but we receive a <class 'dict'> input: {'age': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[35],
[62],
[44],
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array([[1],
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[1],
[0],
[0],
[0],
[1],
[1],
[1],
[1],
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array([[4],
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[2],
[4],
[3],
[4],
[4],
[4],
[3],
[3],
[4],
[4],
[3],
[3]])>, 'trestbps': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[126],
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[120],
[180],
[106],
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[118],
[129],
[142],
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[140],
[165],
[110],
[128],
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[110],
[140],
[140],
[150],
[120],
[130],
[120]])>, 'chol': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[282],
[263],
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[223],
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[277],
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[169],
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array([[0],
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[0],
[0],
[0],
[0],
[1],
[0]])>, 'restecg': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[2],
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[2],
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[2],
[0],
[2],
[0],
[2],
[0]])>, 'thalach': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[156],
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[170],
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[142],
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[111],
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[171],
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[105],
[136],
[159],
[142],
[163],
[128],
[144],
[173],
[170]])>, 'exang': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[1],
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[1],
[1],
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[0],
[0],
[1],
[1],
[0],
[0],
[0],
[0],
[1],
[0],
[0]])>, 'oldpeak': <tf.Tensor: shape=(32, 1), dtype=float64, numpy=
array([[0\. ],
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[0\. ],
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[0\. ],
[0.3],
[1.8],
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[0.2],
[3\. ],
[0\. ],
[1.5],
[0.6],
[2.6],
[2.8],
[0\. ],
[0\. ]])>, 'slope': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[1],
[2],
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[1],
[1],
[1],
[2],
[1],
[1],
[1],
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[2],
[2],
[1],
[2],
[2],
[1],
[1],
[2],
[2],
[3],
[1],
[1]])>, 'ca': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[0],
[1],
[0],
[0],
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[2],
[3],
[1],
[0],
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[3],
[0],
[1],
[0],
[0],
[1],
[1],
[0],
[0],
[3],
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array([[b'reversible'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'reversible'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'fixed'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'reversible'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'reversible'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'reversible'],
[b'fixed'],
[b'normal'],
[b'normal']], dtype=object)>}
Consider rewriting this model with the Functional API.
WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor, but we receive a <class 'dict'> input: {'age': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[57],
[66],
[47],
[60],
[37],
[59],
[54],
[46],
[62],
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[68],
[53],
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[54],
[54],
[47],
[71],
[51],
[58],
[34],
[59],
[52],
[62],
[61],
[42],
[64],
[51],
[55],
[60]])>, 'sex': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[1],
[1],
[1],
[1],
[1],
[1],
[1],
[1],
[1],
[1],
[0],
[1],
[1],
[1],
[1],
[0],
[1],
[1],
[1],
[0],
[0],
[1],
[0],
[1],
[1],
[1],
[1],
[1],
[1],
[0],
[1],
[1]])>, 'cp': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[4],
[4],
[4],
[4],
[3],
[4],
[4],
[4],
[4],
[1],
[3],
[4],
[4],
[4],
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[4],
[2],
[4],
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[3],
[4],
[2],
[4],
[2],
[2],
[4],
[3],
[3],
[4],
[4],
[4]])>, 'trestbps': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[110],
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[112],
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[130],
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[140],
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[138],
[134],
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[148],
[130],
[140],
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[140],
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array([[335],
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[250],
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[281],
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[180],
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array([[0],
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array([[0],
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[0],
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[0],
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[0],
[2],
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[0],
[0],
[0],
[0],
[2]])>, 'thalach': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[143],
[151],
[143],
[160],
[187],
[142],
[160],
[120],
[ 99],
[178],
[115],
[ 95],
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[126],
[136],
[156],
[109],
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[162],
[149],
[131],
[192],
[182],
[158],
[103],
[161],
[150],
[158],
[142],
[111],
[142]])>, 'exang': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[1],
[0],
[0],
[1],
[0],
[1],
[0],
[1],
[1],
[1],
[0],
[1],
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[0],
[1],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[1],
[1],
[1]])>, 'oldpeak': <tf.Tensor: shape=(32, 1), dtype=float64, numpy=
array([[3\. ],
[0.4],
[0.1],
[1.4],
[3.5],
[1.2],
[1.2],
[1.8],
[1.8],
[1.4],
[1.5],
[2\. ],
[0.4],
[0\. ],
[2.8],
[0\. ],
[0\. ],
[2.2],
[0\. ],
[0.4],
[0.5],
[2.2],
[0.7],
[0\. ],
[0.8],
[1.4],
[0\. ],
[0\. ],
[0\. ],
[1.2],
[5.6],
[2.8]])>, 'slope': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[2],
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[1],
[3],
[2],
[1],
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[1],
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[1],
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[1],
[1],
[2],
[3],
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array([[1],
[0],
[0],
[2],
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array([[b'reversible'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'reversible'],
[b'reversible'],
[b'reversible'],
[b'normal'],
[b'reversible'],
[b'fixed'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'reversible'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'reversible'],
[b'reversible']], dtype=object)>}
Consider rewriting this model with the Functional API.
5/7 [====================>.........] - ETA: 0s - loss: 0.6322 - accuracy: 0.6750WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor, but we receive a <class 'dict'> input: {'age': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[70],
[45],
[47],
[57],
[43],
[61],
[42],
[67],
[46],
[55],
[51],
[45],
[59],
[56],
[55],
[70],
[41],
[59],
[57],
[60],
[43],
[42],
[67],
[67],
[38],
[74],
[56],
[57],
[52],
[47],
[54],
[58]])>, 'sex': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[1],
[1],
[1],
[1],
[0],
[1],
[1],
[1],
[1],
[1],
[1],
[0],
[1],
[1],
[0],
[1],
[1],
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[1],
[1],
[1],
[1],
[0],
[1],
[1],
[0],
[1],
[1],
[1],
[1],
[0],
[1]])>, 'cp': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[3],
[4],
[3],
[3],
[3],
[4],
[4],
[4],
[2],
[4],
[3],
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[2],
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[4],
[1],
[3],
[3],
[3]])>, 'trestbps': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[160],
[104],
[138],
[128],
[122],
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[101],
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[120],
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[120],
[120],
[132],
[150],
[118],
[108],
[110],
[105]])>, 'chol': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[269],
[208],
[257],
[229],
[213],
[166],
[315],
[237],
[197],
[353],
[245],
[160],
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[193],
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[322],
[235],
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[177],
[295],
[277],
[254],
[231],
[269],
[184],
[276],
[186],
[243],
[214],
[240]])>, 'fbs': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[1],
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[1],
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[0],
[0],
[1],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
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array([[0],
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[2],
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[0],
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[2]])>, 'thalach': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[112],
[148],
[156],
[150],
[165],
[125],
[125],
[ 71],
[156],
[132],
[166],
[138],
[157],
[162],
[130],
[109],
[153],
[145],
[168],
[144],
[120],
[162],
[172],
[163],
[182],
[121],
[105],
[112],
[190],
[152],
[158],
[154]])>, 'exang': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[1],
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[0],
[0],
[0],
[1],
[1],
[0],
[0],
[1],
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[0],
[1],
[1],
[1],
[1],
[0],
[0],
[0],
[1]])>, 'oldpeak': <tf.Tensor: shape=(32, 1), dtype=float64, numpy=
array([[2.9],
[3\. ],
[0\. ],
[0.4],
[0.2],
[3.6],
[1.8],
[1\. ],
[0\. ],
[1.2],
[2.4],
[0\. ],
[1.6],
[1.9],
[2\. ],
[2.4],
[0\. ],
[4.2],
[0\. ],
[1.4],
[2.5],
[0\. ],
[0\. ],
[0.2],
[3.8],
[0.2],
[2.1],
[0.6],
[0\. ],
[0\. ],
[1.6],
[0.6]])>, 'slope': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[2],
[2],
[1],
[2],
[2],
[2],
[2],
[2],
[1],
[2],
[2],
[2],
[1],
[2],
[2],
[2],
[1],
[3],
[1],
[1],
[2],
[1],
[1],
[2],
[2],
[1],
[2],
[2],
[2],
[1],
[2],
[2]])>, 'ca': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[1],
[0],
[0],
[1],
[0],
[1],
[0],
[0],
[0],
[1],
[0],
[0],
[0],
[0],
[1],
[3],
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[0],
[0],
[1],
[0],
[0],
[1],
[2],
[0],
[1],
[1],
[1],
[0],
[0],
[0],
[0]])>, 'thal': <tf.Tensor: shape=(32, 1), dtype=string, numpy=
array([[b'reversible'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'fixed'],
[b'normal'],
[b'reversible'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'reversible'],
[b'reversible'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'reversible'],
[b'normal'],
[b'fixed'],
[b'fixed'],
[b'fixed'],
[b'normal'],
[b'normal'],
[b'reversible']], dtype=object)>}
Consider rewriting this model with the Functional API.
WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor, but we receive a <class 'dict'> input: {'age': <tf.Tensor: shape=(1, 1), dtype=int64, numpy=array([[45]])>, 'sex': <tf.Tensor: shape=(1, 1), dtype=int64, numpy=array([[0]])>, 'cp': <tf.Tensor: shape=(1, 1), dtype=int64, numpy=array([[4]])>, 'trestbps': <tf.Tensor: shape=(1, 1), dtype=int64, numpy=array([[138]])>, 'chol': <tf.Tensor: shape=(1, 1), dtype=int64, numpy=array([[236]])>, 'fbs': <tf.Tensor: shape=(1, 1), dtype=int64, numpy=array([[0]])>, 'restecg': <tf.Tensor: shape=(1, 1), dtype=int64, numpy=array([[2]])>, 'thalach': <tf.Tensor: shape=(1, 1), dtype=int64, numpy=array([[152]])>, 'exang': <tf.Tensor: shape=(1, 1), dtype=int64, numpy=array([[1]])>, 'oldpeak': <tf.Tensor: shape=(1, 1), dtype=float64, numpy=array([[0.2]])>, 'slope': <tf.Tensor: shape=(1, 1), dtype=int64, numpy=array([[2]])>, 'ca': <tf.Tensor: shape=(1, 1), dtype=int64, numpy=array([[0]])>, 'thal': <tf.Tensor: shape=(1, 1), dtype=string, numpy=array([[b'normal']], dtype=object)>}
Consider rewriting this model with the Functional API.
7/7 [==============================] - ETA: 0s - loss: 0.6209 - accuracy: 0.6943WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor, but we receive a <class 'dict'> input: {'age': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[62],
[65],
[60],
[35],
[48],
[66],
[42],
[44],
[67],
[71],
[45],
[65],
[52],
[76],
[48],
[51],
[61],
[51],
[66],
[51],
[60],
[52],
[49],
[57],
[54],
[68],
[41],
[62],
[59],
[45],
[59],
[55]])>, 'sex': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[0],
[1],
[1],
[1],
[1],
[1],
[0],
[0],
[1],
[0],
[1],
[1],
[1],
[0],
[1],
[1],
[1],
[1],
[0],
[1],
[0],
[1],
[1],
[1],
[1],
[1],
[0],
[0],
[1],
[0],
[1],
[1]])>, 'cp': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[4],
[4],
[3],
[2],
[4],
[4],
[3],
[3],
[3],
[3],
[2],
[4],
[1],
[3],
[3],
[4],
[3],
[3],
[4],
[4],
[3],
[3],
[3],
[4],
[3],
[3],
[2],
[4],
[4],
[2],
[4],
[4]])>, 'trestbps': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[124],
[120],
[140],
[122],
[124],
[112],
[120],
[108],
[152],
[110],
[128],
[110],
[152],
[140],
[124],
[140],
[150],
[100],
[178],
[140],
[120],
[172],
[118],
[110],
[120],
[180],
[105],
[138],
[140],
[130],
[135],
[160]])>, 'chol': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[209],
[177],
[185],
[192],
[274],
[212],
[209],
[141],
[212],
[265],
[308],
[248],
[298],
[197],
[255],
[299],
[243],
[222],
[228],
[261],
[178],
[199],
[149],
[201],
[258],
[274],
[198],
[294],
[177],
[234],
[234],
[289]])>, 'fbs': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[1],
[0],
[0],
[1],
[0],
[1],
[0],
[1],
[0],
[1],
[0],
[1],
[1],
[0],
[0],
[0],
[1],
[0],
[1],
[0],
[0],
[0],
[0]])>, 'restecg': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[0],
[0],
[2],
[0],
[2],
[2],
[0],
[0],
[2],
[2],
[2],
[2],
[0],
[1],
[0],
[0],
[0],
[0],
[0],
[2],
[0],
[0],
[2],
[0],
[2],
[2],
[0],
[0],
[0],
[2],
[0],
[2]])>, 'thalach': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[163],
[140],
[155],
[174],
[166],
[132],
[173],
[175],
[150],
[130],
[170],
[158],
[178],
[116],
[175],
[173],
[137],
[143],
[165],
[186],
[ 96],
[162],
[126],
[126],
[147],
[150],
[168],
[106],
[162],
[175],
[161],
[145]])>, 'exang': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[0],
[0],
[0],
[0],
[0],
[1],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[1],
[1],
[1],
[1],
[1],
[0],
[0],
[0],
[1],
[0],
[1],
[0],
[0],
[1],
[0],
[0],
[1]])>, 'oldpeak': <tf.Tensor: shape=(32, 1), dtype=float64, numpy=
array([[0\. ],
[0.4],
[3\. ],
[0\. ],
[0.5],
[0.1],
[0\. ],
[0.6],
[0.8],
[0\. ],
[0\. ],
[0.6],
[1.2],
[1.1],
[0\. ],
[1.6],
[1\. ],
[1.2],
[1\. ],
[0\. ],
[0\. ],
[0.5],
[0.8],
[1.5],
[0.4],
[1.6],
[0\. ],
[1.9],
[0\. ],
[0.6],
[0.5],
[0.8]])>, 'slope': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[1],
[1],
[2],
[1],
[2],
[1],
[2],
[2],
[2],
[1],
[1],
[1],
[2],
[2],
[1],
[1],
[2],
[2],
[2],
[1],
[1],
[1],
[1],
[2],
[2],
[2],
[1],
[2],
[1],
[2],
[2],
[2]])>, 'ca': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[0],
[0],
[0],
[0],
[0],
[1],
[0],
[0],
[0],
[1],
[0],
[2],
[0],
[0],
[2],
[0],
[0],
[0],
[2],
[0],
[0],
[0],
[3],
[0],
[0],
[0],
[1],
[3],
[1],
[0],
[0],
[1]])>, 'thal': <tf.Tensor: shape=(32, 1), dtype=string, numpy=
array([[b'normal'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'fixed'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'fixed'],
[b'reversible'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'reversible'],
[b'reversible']], dtype=object)>}
Consider rewriting this model with the Functional API.
WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor, but we receive a <class 'dict'> input: {'age': <tf.Tensor: shape=(17, 1), dtype=int64, numpy=
array([[77],
[39],
[60],
[41],
[56],
[51],
[59],
[41],
[60],
[64],
[64],
[70],
[62],
[58],
[58],
[67],
[35]])>, 'sex': <tf.Tensor: shape=(17, 1), dtype=int64, numpy=
array([[1],
[1],
[1],
[0],
[1],
[1],
[1],
[1],
[1],
[0],
[1],
[1],
[0],
[1],
[0],
[1],
[0]])>, 'cp': <tf.Tensor: shape=(17, 1), dtype=int64, numpy=
array([[4],
[4],
[4],
[2],
[3],
[3],
[2],
[2],
[4],
[4],
[4],
[2],
[4],
[4],
[4],
[4],
[4]])>, 'trestbps': <tf.Tensor: shape=(17, 1), dtype=int64, numpy=
array([[125],
[118],
[130],
[130],
[130],
[ 94],
[140],
[120],
[125],
[130],
[120],
[156],
[160],
[125],
[130],
[120],
[138]])>, 'chol': <tf.Tensor: shape=(17, 1), dtype=int64, numpy=
array([[304],
[219],
[206],
[204],
[256],
[227],
[221],
[157],
[258],
[303],
[246],
[245],
[164],
[300],
[197],
[229],
[183]])>, 'fbs': <tf.Tensor: shape=(17, 1), dtype=int64, numpy=
array([[0],
[0],
[0],
[0],
[1],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0]])>, 'restecg': <tf.Tensor: shape=(17, 1), dtype=int64, numpy=
array([[2],
[0],
[2],
[2],
[2],
[0],
[0],
[0],
[2],
[0],
[2],
[2],
[2],
[2],
[0],
[2],
[0]])>, 'thalach': <tf.Tensor: shape=(17, 1), dtype=int64, numpy=
array([[162],
[140],
[132],
[172],
[142],
[154],
[164],
[182],
[141],
[122],
[ 96],
[143],
[145],
[171],
[131],
[129],
[182]])>, 'exang': <tf.Tensor: shape=(17, 1), dtype=int64, numpy=
array([[1],
[0],
[1],
[0],
[1],
[1],
[1],
[0],
[1],
[0],
[1],
[0],
[0],
[0],
[0],
[1],
[0]])>, 'oldpeak': <tf.Tensor: shape=(17, 1), dtype=float64, numpy=
array([[0\. ],
[1.2],
[2.4],
[1.4],
[0.6],
[0\. ],
[0\. ],
[0\. ],
[2.8],
[2\. ],
[2.2],
[0\. ],
[6.2],
[0\. ],
[0.6],
[2.6],
[1.4]])>, 'slope': <tf.Tensor: shape=(17, 1), dtype=int64, numpy=
array([[1],
[2],
[2],
[1],
[2],
[1],
[1],
[1],
[2],
[2],
[3],
[1],
[3],
[1],
[2],
[2],
[1]])>, 'ca': <tf.Tensor: shape=(17, 1), dtype=int64, numpy=
array([[3],
[0],
[2],
[0],
[1],
[1],
[0],
[0],
[1],
[2],
[1],
[0],
[3],
[2],
[0],
[2],
[0]])>, 'thal': <tf.Tensor: shape=(17, 1), dtype=string, numpy=
array([[b'normal'],
[b'reversible'],
[b'reversible'],
[b'normal'],
[b'fixed'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'reversible'],
[b'normal'],
[b'reversible'],
[b'normal']], dtype=object)>}
Consider rewriting this model with the Functional API.
7/7 [==============================] - 0s 43ms/step - loss: 0.6209 - accuracy: 0.6943 - val_loss: 0.6867 - val_accuracy: 0.7347
Epoch 5/5
WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor, but we receive a <class 'dict'> input: {'age': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[60],
[51],
[49],
[64],
[54],
[58],
[42],
[68],
[70],
[58],
[66],
[57],
[57],
[51],
[60],
[59],
[55],
[67],
[49],
[53],
[50],
[54],
[65],
[56],
[48],
[59],
[63],
[50],
[59],
[34],
[54],
[68]])>, 'sex': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[1],
[0],
[1],
[1],
[1],
[0],
[1],
[1],
[1],
[0],
[1],
[1],
[1],
[1],
[1],
[1],
[1],
[1],
[0],
[0],
[1],
[1],
[0],
[0],
[1],
[0],
[0],
[1],
[1],
[0],
[1],
[1]])>, 'cp': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[4],
[3],
[3],
[1],
[2],
[4],
[3],
[4],
[3],
[2],
[2],
[4],
[3],
[3],
[4],
[4],
[4],
[4],
[4],
[4],
[3],
[4],
[3],
[2],
[4],
[4],
[3],
[4],
[4],
[2],
[4],
[3]])>, 'trestbps': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[130],
[130],
[120],
[110],
[108],
[100],
[130],
[144],
[160],
[136],
[160],
[132],
[128],
[110],
[145],
[138],
[132],
[100],
[130],
[130],
[140],
[122],
[140],
[140],
[122],
[174],
[135],
[150],
[110],
[118],
[140],
[118]])>, 'chol': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[253],
[256],
[188],
[211],
[309],
[248],
[180],
[193],
[269],
[319],
[246],
[207],
[229],
[175],
[282],
[271],
[353],
[299],
[269],
[264],
[233],
[286],
[417],
[294],
[222],
[249],
[252],
[243],
[239],
[210],
[239],
[277]])>, 'fbs': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[0],
[0],
[0],
[0],
[0],
[0],
[0],
[1],
[0],
[1],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[1],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0]])>, 'restecg': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[0],
[2],
[0],
[2],
[0],
[2],
[0],
[0],
[0],
[2],
[0],
[0],
[2],
[0],
[2],
[2],
[0],
[2],
[0],
[2],
[0],
[2],
[2],
[2],
[2],
[0],
[2],
[2],
[2],
[0],
[0],
[0]])>, 'thalach': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[144],
[149],
[139],
[144],
[156],
[122],
[150],
[141],
[112],
[152],
[120],
[168],
[150],
[123],
[142],
[182],
[132],
[125],
[163],
[143],
[163],
[116],
[157],
[153],
[186],
[143],
[172],
[128],
[142],
[192],
[160],
[151]])>, 'exang': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[1],
[0],
[0],
[1],
[0],
[0],
[0],
[0],
[1],
[0],
[1],
[1],
[0],
[0],
[1],
[0],
[1],
[1],
[0],
[0],
[0],
[1],
[0],
[0],
[0],
[1],
[0],
[0],
[1],
[0],
[0],
[0]])>, 'oldpeak': <tf.Tensor: shape=(32, 1), dtype=float64, numpy=
array([[1.4],
[0.5],
[2\. ],
[1.8],
[0\. ],
[1\. ],
[0\. ],
[3.4],
[2.9],
[0\. ],
[0\. ],
[0\. ],
[0.4],
[0.6],
[2.8],
[0\. ],
[1.2],
[0.9],
[0\. ],
[0.4],
[0.6],
[3.2],
[0.8],
[1.3],
[0\. ],
[0\. ],
[0\. ],
[2.6],
[1.2],
[0.7],
[1.2],
[1\. ]])>, 'slope': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[1],
[1],
[2],
[2],
[1],
[2],
[1],
[2],
[2],
[1],
[2],
[1],
[2],
[1],
[2],
[1],
[2],
[2],
[1],
[2],
[2],
[2],
[1],
[2],
[1],
[2],
[1],
[2],
[2],
[1],
[1],
[1]])>, 'ca': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[1],
[0],
[3],
[0],
[0],
[0],
[0],
[2],
[1],
[2],
[3],
[0],
[1],
[0],
[2],
[0],
[1],
[2],
[0],
[0],
[1],
[2],
[1],
[0],
[0],
[0],
[0],
[0],
[1],
[0],
[0],
[1]])>, 'thal': <tf.Tensor: shape=(32, 1), dtype=string, numpy=
array([[b'reversible'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'reversible'],
[b'normal'],
[b'fixed'],
[b'reversible'],
[b'reversible'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'reversible']], dtype=object)>}
Consider rewriting this model with the Functional API.
1/7 [===>..........................] - ETA: 0s - loss: 0.8235 - accuracy: 0.6562WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor, but we receive a <class 'dict'> input: {'age': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[47],
[51],
[57],
[54],
[56],
[52],
[42],
[74],
[57],
[58],
[55],
[50],
[56],
[59],
[52],
[51],
[41],
[60],
[34],
[67],
[45],
[62],
[56],
[51],
[57],
[45],
[53],
[64],
[59],
[58],
[56],
[69]])>, 'sex': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[1],
[0],
[1],
[0],
[1],
[1],
[1],
[0],
[0],
[1],
[1],
[1],
[1],
[1],
[1],
[0],
[1],
[0],
[1],
[0],
[1],
[1],
[0],
[1],
[1],
[1],
[1],
[1],
[1],
[1],
[0],
[1]])>, 'cp': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[3],
[3],
[3],
[3],
[4],
[2],
[4],
[2],
[2],
[3],
[4],
[4],
[4],
[1],
[4],
[4],
[3],
[4],
[1],
[3],
[1],
[2],
[4],
[3],
[2],
[4],
[3],
[1],
[1],
[4],
[4],
[1]])>, 'trestbps': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[108],
[140],
[150],
[110],
[132],
[120],
[140],
[120],
[130],
[132],
[140],
[144],
[125],
[170],
[128],
[130],
[112],
[150],
[118],
[152],
[110],
[128],
[134],
[125],
[124],
[115],
[130],
[170],
[178],
[128],
[200],
[160]])>, 'chol': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[243],
[308],
[168],
[214],
[184],
[325],
[226],
[269],
[236],
[224],
[217],
[200],
[249],
[288],
[255],
[305],
[250],
[258],
[182],
[277],
[264],
[208],
[409],
[245],
[261],
[260],
[197],
[227],
[270],
[216],
[288],
[234]])>, 'fbs': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[1],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[1],
[0],
[1],
[0],
[0],
[1],
[0],
[0],
[0],
[1],
[1]])>, 'restecg': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[0],
[2],
[0],
[0],
[2],
[0],
[0],
[2],
[2],
[2],
[0],
[2],
[2],
[2],
[0],
[0],
[0],
[2],
[2],
[0],
[0],
[2],
[2],
[2],
[0],
[2],
[2],
[2],
[2],
[2],
[2],
[2]])>, 'thalach': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[152],
[142],
[174],
[158],
[105],
[172],
[178],
[121],
[174],
[173],
[111],
[126],
[144],
[159],
[161],
[142],
[179],
[157],
[174],
[172],
[132],
[140],
[150],
[166],
[141],
[185],
[152],
[155],
[145],
[131],
[133],
[131]])>, 'exang': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[0],
[0],
[0],
[0],
[1],
[0],
[0],
[1],
[0],
[0],
[1],
[1],
[1],
[0],
[1],
[1],
[0],
[0],
[0],
[0],
[0],
[0],
[1],
[0],
[0],
[0],
[0],
[0],
[0],
[1],
[1],
[0]])>, 'oldpeak': <tf.Tensor: shape=(32, 1), dtype=float64, numpy=
array([[0\. ],
[1.5],
[1.6],
[1.6],
[2.1],
[0.2],
[0\. ],
[0.2],
[0\. ],
[3.2],
[5.6],
[0.9],
[1.2],
[0.2],
[0\. ],
[1.2],
[0\. ],
[2.6],
[0\. ],
[0\. ],
[1.2],
[0\. ],
[1.9],
[2.4],
[0.3],
[0\. ],
[1.2],
[0.6],
[4.2],
[2.2],
[4\. ],
[0.1]])>, 'slope': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[1],
[1],
[1],
[2],
[2],
[1],
[1],
[1],
[2],
[1],
[3],
[2],
[2],
[2],
[1],
[2],
[1],
[2],
[1],
[1],
[2],
[1],
[2],
[2],
[1],
[1],
[3],
[2],
[3],
[2],
[3],
[2]])>, 'ca': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[0],
[1],
[0],
[0],
[1],
[0],
[0],
[1],
[1],
[2],
[0],
[0],
[1],
[0],
[1],
[0],
[0],
[2],
[0],
[1],
[0],
[0],
[2],
[0],
[0],
[0],
[0],
[0],
[0],
[3],
[2],
[1]])>, 'thal': <tf.Tensor: shape=(32, 1), dtype=string, numpy=
array([[b'normal'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'fixed'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'reversible'],
[b'reversible'],
[b'normal'],
[b'reversible'],
[b'reversible'],
[b'reversible'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'reversible'],
[b'reversible'],
[b'reversible'],
[b'normal']], dtype=object)>}
Consider rewriting this model with the Functional API.
WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor, but we receive a <class 'dict'> input: {'age': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[37],
[55],
[68],
[29],
[57],
[45],
[44],
[44],
[45],
[54],
[49],
[58],
[52],
[46],
[58],
[41],
[60],
[60],
[63],
[40],
[58],
[57],
[44],
[52],
[43],
[66],
[51],
[63],
[68],
[43],
[67],
[62]])>, 'sex': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[1],
[0],
[1],
[1],
[0],
[0],
[1],
[1],
[0],
[1],
[0],
[1],
[1],
[1],
[1],
[1],
[1],
[0],
[1],
[1],
[1],
[1],
[1],
[1],
[1],
[1],
[1],
[1],
[0],
[1],
[1],
[0]])>, 'cp': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[3],
[4],
[0],
[2],
[4],
[2],
[2],
[4],
[4],
[4],
[2],
[4],
[1],
[4],
[2],
[2],
[4],
[3],
[4],
[4],
[4],
[2],
[3],
[4],
[4],
[4],
[1],
[4],
[3],
[4],
[4],
[3]])>, 'trestbps': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[130],
[128],
[144],
[130],
[120],
[112],
[120],
[112],
[138],
[110],
[134],
[150],
[118],
[140],
[120],
[135],
[117],
[102],
[140],
[110],
[114],
[154],
[140],
[125],
[150],
[160],
[125],
[130],
[120],
[110],
[160],
[130]])>, 'chol': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[250],
[205],
[193],
[204],
[354],
[160],
[263],
[290],
[236],
[206],
[271],
[270],
[186],
[311],
[284],
[203],
[230],
[318],
[187],
[167],
[318],
[232],
[235],
[212],
[247],
[228],
[213],
[254],
[211],
[211],
[286],
[263]])>, 'fbs': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[0],
[0],
[1],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[1],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0]])>, 'restecg': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[0],
[1],
[1],
[2],
[0],
[0],
[0],
[2],
[2],
[2],
[0],
[2],
[2],
[0],
[2],
[0],
[0],
[0],
[2],
[2],
[1],
[2],
[2],
[0],
[0],
[2],
[2],
[2],
[2],
[0],
[2],
[0]])>, 'thalach': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[187],
[130],
[141],
[202],
[163],
[138],
[173],
[153],
[152],
[108],
[162],
[111],
[190],
[120],
[160],
[132],
[160],
[160],
[144],
[114],
[140],
[164],
[180],
[168],
[171],
[138],
[125],
[147],
[115],
[161],
[108],
[ 97]])>, 'exang': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[0],
[1],
[0],
[0],
[1],
[0],
[0],
[0],
[1],
[1],
[0],
[1],
[0],
[1],
[0],
[0],
[1],
[0],
[1],
[1],
[0],
[0],
[0],
[0],
[0],
[0],
[1],
[0],
[0],
[0],
[1],
[0]])>, 'oldpeak': <tf.Tensor: shape=(32, 1), dtype=float64, numpy=
array([[3.5],
[2\. ],
[3.4],
[0\. ],
[0.6],
[0\. ],
[0\. ],
[0\. ],
[0.2],
[0\. ],
[0\. ],
[0.8],
[0\. ],
[1.8],
[1.8],
[0\. ],
[1.4],
[0\. ],
[4\. ],
[2\. ],
[4.4],
[0\. ],
[0\. ],
[1\. ],
[1.5],
[2.3],
[1.4],
[1.4],
[1.5],
[0\. ],
[1.5],
[1.2]])>, 'slope': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[3],
[2],
[1],
[1],
[1],
[2],
[1],
[1],
[2],
[2],
[2],
[1],
[2],
[2],
[2],
[2],
[1],
[1],
[1],
[2],
[3],
[1],
[1],
[1],
[1],
[1],
[1],
[2],
[2],
[1],
[2],
[2]])>, 'ca': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[0],
[1],
[2],
[0],
[0],
[0],
[0],
[1],
[0],
[1],
[0],
[0],
[0],
[2],
[0],
[0],
[2],
[1],
[2],
[0],
[3],
[1],
[0],
[2],
[0],
[0],
[1],
[1],
[0],
[0],
[3],
[1]])>, 'thal': <tf.Tensor: shape=(32, 1), dtype=string, numpy=
array([[b'normal'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'fixed'],
[b'reversible'],
[b'normal'],
[b'fixed'],
[b'reversible'],
[b'normal'],
[b'reversible'],
[b'reversible'],
[b'fixed'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'fixed'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'reversible']], dtype=object)>}
Consider rewriting this model with the Functional API.
3/7 [===========>..................] - ETA: 0s - loss: 0.7040 - accuracy: 0.6979WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor, but we receive a <class 'dict'> input: {'age': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[46],
[60],
[67],
[62],
[59],
[54],
[54],
[61],
[57],
[64],
[42],
[59],
[62],
[67],
[38],
[43],
[54],
[57],
[59],
[62],
[58],
[70],
[57],
[45],
[53],
[53],
[51],
[48],
[41],
[65],
[53],
[64]])>, 'sex': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[0],
[1],
[1],
[0],
[1],
[0],
[1],
[1],
[1],
[0],
[1],
[1],
[1],
[0],
[1],
[1],
[1],
[1],
[1],
[0],
[0],
[1],
[1],
[1],
[1],
[1],
[1],
[1],
[1],
[1],
[1],
[1]])>, 'cp': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[3],
[4],
[4],
[4],
[3],
[3],
[3],
[4],
[3],
[4],
[1],
[0],
[2],
[3],
[1],
[4],
[4],
[4],
[3],
[4],
[1],
[4],
[4],
[4],
[4],
[3],
[4],
[2],
[2],
[1],
[4],
[3]])>, 'trestbps': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[142],
[140],
[125],
[150],
[150],
[135],
[150],
[138],
[150],
[180],
[148],
[164],
[120],
[115],
[120],
[115],
[120],
[140],
[126],
[140],
[150],
[130],
[165],
[142],
[142],
[130],
[140],
[110],
[110],
[138],
[123],
[140]])>, 'chol': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[177],
[293],
[254],
[244],
[212],
[304],
[232],
[166],
[126],
[325],
[244],
[176],
[281],
[564],
[231],
[303],
[188],
[192],
[218],
[268],
[283],
[322],
[289],
[309],
[226],
[246],
[298],
[229],
[235],
[282],
[282],
[335]])>, 'fbs': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[0],
[0],
[1],
[0],
[1],
[1],
[0],
[0],
[1],
[0],
[0],
[1],
[0],
[0],
[0],
[0],
[0],
[0],
[1],
[0],
[1],
[0],
[1],
[0],
[0],
[1],
[0],
[0],
[0],
[1],
[0],
[0]])>, 'restecg': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[2],
[2],
[0],
[0],
[0],
[0],
[2],
[2],
[0],
[0],
[2],
[0],
[2],
[2],
[0],
[0],
[0],
[0],
[0],
[2],
[2],
[2],
[2],
[2],
[2],
[2],
[0],
[0],
[0],
[2],
[0],
[0]])>, 'thalach': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[160],
[170],
[163],
[154],
[157],
[170],
[165],
[125],
[173],
[154],
[178],
[ 90],
[103],
[160],
[182],
[181],
[113],
[148],
[134],
[160],
[162],
[109],
[124],
[147],
[111],
[173],
[122],
[168],
[153],
[174],
[ 95],
[158]])>, 'exang': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[1],
[0],
[0],
[1],
[0],
[0],
[0],
[1],
[0],
[1],
[0],
[0],
[0],
[0],
[1],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[1],
[1],
[0],
[1],
[0],
[0],
[0],
[1],
[0]])>, 'oldpeak': <tf.Tensor: shape=(32, 1), dtype=float64, numpy=
array([[1.4],
[1.2],
[0.2],
[1.4],
[1.6],
[0\. ],
[1.6],
[3.6],
[0.2],
[0\. ],
[0.8],
[1\. ],
[1.4],
[1.6],
[3.8],
[1.2],
[1.4],
[0.4],
[2.2],
[3.6],
[1\. ],
[2.4],
[1\. ],
[0\. ],
[0\. ],
[0\. ],
[4.2],
[1\. ],
[0\. ],
[1.4],
[2\. ],
[0\. ]])>, 'slope': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[3],
[2],
[2],
[2],
[1],
[1],
[1],
[2],
[1],
[1],
[1],
[1],
[2],
[2],
[2],
[2],
[2],
[2],
[2],
[3],
[1],
[2],
[2],
[2],
[1],
[1],
[2],
[3],
[1],
[2],
[2],
[1]])>, 'ca': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[0],
[2],
[2],
[0],
[0],
[0],
[0],
[1],
[1],
[0],
[2],
[2],
[1],
[0],
[0],
[0],
[1],
[0],
[1],
[2],
[0],
[3],
[3],
[3],
[0],
[3],
[3],
[0],
[0],
[1],
[2],
[0]])>, 'thal': <tf.Tensor: shape=(32, 1), dtype=string, numpy=
array([[b'normal'],
[b'reversible'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'1'],
[b'reversible'],
[b'reversible'],
[b'reversible'],
[b'normal'],
[b'reversible'],
[b'fixed'],
[b'fixed'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'reversible'],
[b'reversible'],
[b'normal'],
[b'reversible'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'normal']], dtype=object)>}
Consider rewriting this model with the Functional API.
WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor, but we receive a <class 'dict'> input: {'age': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[46],
[57],
[44],
[42],
[57],
[43],
[49],
[41],
[44],
[63],
[56],
[62],
[52],
[43],
[54],
[58],
[63],
[71],
[55],
[62],
[46],
[52],
[47],
[61],
[40],
[50],
[59],
[47],
[47],
[62],
[57],
[64]])>, 'sex': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[1],
[1],
[1],
[1],
[1],
[1],
[1],
[1],
[0],
[0],
[1],
[0],
[1],
[1],
[1],
[1],
[1],
[0],
[0],
[1],
[0],
[1],
[1],
[0],
[1],
[1],
[1],
[1],
[1],
[1],
[1],
[1]])>, 'cp': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[3],
[4],
[4],
[4],
[4],
[4],
[2],
[3],
[3],
[4],
[1],
[4],
[4],
[3],
[4],
[4],
[4],
[2],
[4],
[3],
[4],
[2],
[4],
[4],
[1],
[3],
[1],
[3],
[3],
[4],
[4],
[4]])>, 'trestbps': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[150],
[152],
[120],
[136],
[110],
[120],
[130],
[130],
[118],
[124],
[120],
[140],
[112],
[130],
[110],
[100],
[130],
[160],
[180],
[130],
[138],
[134],
[112],
[130],
[140],
[129],
[134],
[138],
[130],
[120],
[150],
[145]])>, 'chol': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[231],
[274],
[169],
[315],
[335],
[177],
[266],
[214],
[242],
[197],
[193],
[394],
[230],
[315],
[239],
[234],
[330],
[302],
[327],
[231],
[243],
[201],
[204],
[330],
[199],
[196],
[204],
[257],
[253],
[267],
[276],
[212]])>, 'fbs': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[1],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0]])>, 'restecg': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[0],
[0],
[0],
[0],
[0],
[2],
[0],
[2],
[0],
[0],
[2],
[2],
[0],
[0],
[0],
[0],
[2],
[0],
[1],
[0],
[2],
[0],
[0],
[2],
[0],
[0],
[0],
[2],
[0],
[0],
[2],
[2]])>, 'thalach': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[147],
[ 88],
[144],
[125],
[143],
[120],
[171],
[168],
[149],
[136],
[162],
[157],
[160],
[162],
[126],
[156],
[132],
[162],
[117],
[146],
[152],
[158],
[143],
[169],
[178],
[163],
[162],
[156],
[179],
[ 99],
[112],
[132]])>, 'exang': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[0],
[1],
[1],
[1],
[1],
[1],
[0],
[0],
[0],
[1],
[0],
[0],
[0],
[0],
[1],
[0],
[1],
[0],
[1],
[0],
[1],
[0],
[0],
[0],
[1],
[0],
[0],
[0],
[0],
[1],
[1],
[0]])>, 'oldpeak': <tf.Tensor: shape=(32, 1), dtype=float64, numpy=
array([[3.6],
[1.2],
[2.8],
[1.8],
[3\. ],
[2.5],
[0.6],
[2\. ],
[0.3],
[0\. ],
[1.9],
[1.2],
[0\. ],
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[2.8],
[0.1],
[1.8],
[0.4],
[3.4],
[1.8],
[0\. ],
[0.8],
[0.1],
[0\. ],
[1.4],
[0\. ],
[0.8],
[0\. ],
[0\. ],
[1.8],
[0.6],
[2\. ]])>, 'slope': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[2],
[2],
[3],
[2],
[2],
[2],
[1],
[2],
[2],
[2],
[2],
[2],
[1],
[1],
[2],
[1],
[1],
[1],
[2],
[2],
[2],
[1],
[1],
[1],
[1],
[1],
[1],
[1],
[1],
[2],
[2],
[2]])>, 'ca': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[0],
[1],
[0],
[0],
[1],
[0],
[0],
[0],
[1],
[0],
[0],
[0],
[1],
[1],
[1],
[1],
[3],
[2],
[0],
[3],
[0],
[1],
[0],
[0],
[0],
[0],
[2],
[0],
[0],
[2],
[1],
[2]])>, 'thal': <tf.Tensor: shape=(32, 1), dtype=string, numpy=
array([[b'normal'],
[b'reversible'],
[b'fixed'],
[b'fixed'],
[b'reversible'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'reversible'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'fixed'],
[b'fixed']], dtype=object)>}
Consider rewriting this model with the Functional API.
5/7 [====================>.........] - ETA: 0s - loss: 0.6946 - accuracy: 0.7000WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor, but we receive a <class 'dict'> input: {'age': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[46],
[61],
[58],
[42],
[42],
[50],
[45],
[43],
[35],
[56],
[57],
[37],
[41],
[58],
[50],
[56],
[67],
[44],
[64],
[44],
[64],
[44],
[42],
[43],
[59],
[67],
[64],
[50],
[46],
[66],
[55],
[39]])>, 'sex': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[1],
[1],
[1],
[1],
[1],
[0],
[1],
[0],
[1],
[1],
[0],
[0],
[0],
[1],
[0],
[1],
[1],
[1],
[0],
[1],
[1],
[1],
[0],
[0],
[1],
[0],
[1],
[0],
[1],
[1],
[0],
[0]])>, 'cp': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[2],
[4],
[4],
[3],
[2],
[2],
[4],
[4],
[4],
[2],
[4],
[3],
[2],
[3],
[3],
[4],
[4],
[2],
[3],
[3],
[4],
[2],
[4],
[3],
[4],
[4],
[3],
[4],
[4],
[4],
[2],
[3]])>, 'trestbps': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[101],
[148],
[128],
[120],
[120],
[120],
[104],
[132],
[126],
[120],
[128],
[120],
[126],
[105],
[120],
[130],
[120],
[120],
[140],
[130],
[128],
[130],
[102],
[122],
[164],
[106],
[125],
[110],
[120],
[120],
[132],
[ 94]])>, 'chol': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[197],
[203],
[259],
[240],
[295],
[244],
[208],
[341],
[282],
[240],
[303],
[215],
[306],
[240],
[219],
[283],
[237],
[220],
[313],
[233],
[263],
[219],
[265],
[213],
[176],
[223],
[309],
[254],
[249],
[302],
[342],
[199]])>, 'fbs': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[1],
[0],
[0],
[1],
[0],
[0],
[0],
[1],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[1],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[1],
[0],
[0],
[0],
[0],
[0],
[0],
[0]])>, 'restecg': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[0],
[0],
[2],
[0],
[0],
[0],
[2],
[2],
[2],
[0],
[2],
[0],
[0],
[2],
[0],
[2],
[0],
[0],
[0],
[0],
[0],
[2],
[2],
[0],
[2],
[0],
[0],
[2],
[2],
[2],
[0],
[0]])>, 'thalach': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[156],
[161],
[130],
[194],
[162],
[162],
[148],
[136],
[156],
[169],
[159],
[170],
[163],
[154],
[158],
[103],
[ 71],
[170],
[133],
[179],
[105],
[188],
[122],
[165],
[ 90],
[142],
[131],
[159],
[144],
[151],
[166],
[179]])>, 'exang': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[0],
[0],
[1],
[0],
[0],
[0],
[1],
[1],
[1],
[0],
[0],
[0],
[0],
[1],
[0],
[1],
[0],
[0],
[0],
[1],
[1],
[0],
[0],
[0],
[0],
[0],
[1],
[0],
[0],
[0],
[0],
[0]])>, 'oldpeak': <tf.Tensor: shape=(32, 1), dtype=float64, numpy=
array([[0\. ],
[0\. ],
[3\. ],
[0.8],
[0\. ],
[1.1],
[3\. ],
[3\. ],
[0\. ],
[0\. ],
[0\. ],
[0\. ],
[0\. ],
[0.6],
[1.6],
[1.6],
[1\. ],
[0\. ],
[0.2],
[0.4],
[0.2],
[0\. ],
[0.6],
[0.2],
[1\. ],
[0.3],
[1.8],
[0\. ],
[0.8],
[0.4],
[1.2],
[0\. ]])>, 'slope': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[1],
[1],
[2],
[3],
[1],
[1],
[2],
[2],
[1],
[3],
[1],
[1],
[1],
[2],
[2],
[3],
[2],
[1],
[1],
[1],
[2],
[1],
[2],
[2],
[2],
[1],
[2],
[1],
[1],
[2],
[1],
[1]])>, 'ca': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[0],
[1],
[2],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[1],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[1],
[0],
[0],
[0],
[2],
[2],
[0],
[0],
[0],
[0],
[0],
[0]])>, 'thal': <tf.Tensor: shape=(32, 1), dtype=string, numpy=
array([[b'reversible'],
[b'reversible'],
[b'reversible'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'fixed'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'normal']], dtype=object)>}
Consider rewriting this model with the Functional API.
WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor, but we receive a <class 'dict'> input: {'age': <tf.Tensor: shape=(1, 1), dtype=int64, numpy=array([[54]])>, 'sex': <tf.Tensor: shape=(1, 1), dtype=int64, numpy=array([[1]])>, 'cp': <tf.Tensor: shape=(1, 1), dtype=int64, numpy=array([[4]])>, 'trestbps': <tf.Tensor: shape=(1, 1), dtype=int64, numpy=array([[124]])>, 'chol': <tf.Tensor: shape=(1, 1), dtype=int64, numpy=array([[266]])>, 'fbs': <tf.Tensor: shape=(1, 1), dtype=int64, numpy=array([[0]])>, 'restecg': <tf.Tensor: shape=(1, 1), dtype=int64, numpy=array([[2]])>, 'thalach': <tf.Tensor: shape=(1, 1), dtype=int64, numpy=array([[109]])>, 'exang': <tf.Tensor: shape=(1, 1), dtype=int64, numpy=array([[1]])>, 'oldpeak': <tf.Tensor: shape=(1, 1), dtype=float64, numpy=array([[2.2]])>, 'slope': <tf.Tensor: shape=(1, 1), dtype=int64, numpy=array([[2]])>, 'ca': <tf.Tensor: shape=(1, 1), dtype=int64, numpy=array([[1]])>, 'thal': <tf.Tensor: shape=(1, 1), dtype=string, numpy=array([[b'reversible']], dtype=object)>}
Consider rewriting this model with the Functional API.
7/7 [==============================] - ETA: 0s - loss: 0.6719 - accuracy: 0.7098WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor, but we receive a <class 'dict'> input: {'age': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[62],
[65],
[60],
[35],
[48],
[66],
[42],
[44],
[67],
[71],
[45],
[65],
[52],
[76],
[48],
[51],
[61],
[51],
[66],
[51],
[60],
[52],
[49],
[57],
[54],
[68],
[41],
[62],
[59],
[45],
[59],
[55]])>, 'sex': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[0],
[1],
[1],
[1],
[1],
[1],
[0],
[0],
[1],
[0],
[1],
[1],
[1],
[0],
[1],
[1],
[1],
[1],
[0],
[1],
[0],
[1],
[1],
[1],
[1],
[1],
[0],
[0],
[1],
[0],
[1],
[1]])>, 'cp': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[4],
[4],
[3],
[2],
[4],
[4],
[3],
[3],
[3],
[3],
[2],
[4],
[1],
[3],
[3],
[4],
[3],
[3],
[4],
[4],
[3],
[3],
[3],
[4],
[3],
[3],
[2],
[4],
[4],
[2],
[4],
[4]])>, 'trestbps': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[124],
[120],
[140],
[122],
[124],
[112],
[120],
[108],
[152],
[110],
[128],
[110],
[152],
[140],
[124],
[140],
[150],
[100],
[178],
[140],
[120],
[172],
[118],
[110],
[120],
[180],
[105],
[138],
[140],
[130],
[135],
[160]])>, 'chol': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[209],
[177],
[185],
[192],
[274],
[212],
[209],
[141],
[212],
[265],
[308],
[248],
[298],
[197],
[255],
[299],
[243],
[222],
[228],
[261],
[178],
[199],
[149],
[201],
[258],
[274],
[198],
[294],
[177],
[234],
[234],
[289]])>, 'fbs': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[1],
[0],
[0],
[1],
[0],
[1],
[0],
[1],
[0],
[1],
[0],
[1],
[1],
[0],
[0],
[0],
[1],
[0],
[1],
[0],
[0],
[0],
[0]])>, 'restecg': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[0],
[0],
[2],
[0],
[2],
[2],
[0],
[0],
[2],
[2],
[2],
[2],
[0],
[1],
[0],
[0],
[0],
[0],
[0],
[2],
[0],
[0],
[2],
[0],
[2],
[2],
[0],
[0],
[0],
[2],
[0],
[2]])>, 'thalach': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[163],
[140],
[155],
[174],
[166],
[132],
[173],
[175],
[150],
[130],
[170],
[158],
[178],
[116],
[175],
[173],
[137],
[143],
[165],
[186],
[ 96],
[162],
[126],
[126],
[147],
[150],
[168],
[106],
[162],
[175],
[161],
[145]])>, 'exang': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[0],
[0],
[0],
[0],
[0],
[1],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[1],
[1],
[1],
[1],
[1],
[0],
[0],
[0],
[1],
[0],
[1],
[0],
[0],
[1],
[0],
[0],
[1]])>, 'oldpeak': <tf.Tensor: shape=(32, 1), dtype=float64, numpy=
array([[0\. ],
[0.4],
[3\. ],
[0\. ],
[0.5],
[0.1],
[0\. ],
[0.6],
[0.8],
[0\. ],
[0\. ],
[0.6],
[1.2],
[1.1],
[0\. ],
[1.6],
[1\. ],
[1.2],
[1\. ],
[0\. ],
[0\. ],
[0.5],
[0.8],
[1.5],
[0.4],
[1.6],
[0\. ],
[1.9],
[0\. ],
[0.6],
[0.5],
[0.8]])>, 'slope': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[1],
[1],
[2],
[1],
[2],
[1],
[2],
[2],
[2],
[1],
[1],
[1],
[2],
[2],
[1],
[1],
[2],
[2],
[2],
[1],
[1],
[1],
[1],
[2],
[2],
[2],
[1],
[2],
[1],
[2],
[2],
[2]])>, 'ca': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[0],
[0],
[0],
[0],
[0],
[1],
[0],
[0],
[0],
[1],
[0],
[2],
[0],
[0],
[2],
[0],
[0],
[0],
[2],
[0],
[0],
[0],
[3],
[0],
[0],
[0],
[1],
[3],
[1],
[0],
[0],
[1]])>, 'thal': <tf.Tensor: shape=(32, 1), dtype=string, numpy=
array([[b'normal'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'fixed'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'fixed'],
[b'reversible'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'reversible'],
[b'reversible']], dtype=object)>}
Consider rewriting this model with the Functional API.
WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor, but we receive a <class 'dict'> input: {'age': <tf.Tensor: shape=(17, 1), dtype=int64, numpy=
array([[77],
[39],
[60],
[41],
[56],
[51],
[59],
[41],
[60],
[64],
[64],
[70],
[62],
[58],
[58],
[67],
[35]])>, 'sex': <tf.Tensor: shape=(17, 1), dtype=int64, numpy=
array([[1],
[1],
[1],
[0],
[1],
[1],
[1],
[1],
[1],
[0],
[1],
[1],
[0],
[1],
[0],
[1],
[0]])>, 'cp': <tf.Tensor: shape=(17, 1), dtype=int64, numpy=
array([[4],
[4],
[4],
[2],
[3],
[3],
[2],
[2],
[4],
[4],
[4],
[2],
[4],
[4],
[4],
[4],
[4]])>, 'trestbps': <tf.Tensor: shape=(17, 1), dtype=int64, numpy=
array([[125],
[118],
[130],
[130],
[130],
[ 94],
[140],
[120],
[125],
[130],
[120],
[156],
[160],
[125],
[130],
[120],
[138]])>, 'chol': <tf.Tensor: shape=(17, 1), dtype=int64, numpy=
array([[304],
[219],
[206],
[204],
[256],
[227],
[221],
[157],
[258],
[303],
[246],
[245],
[164],
[300],
[197],
[229],
[183]])>, 'fbs': <tf.Tensor: shape=(17, 1), dtype=int64, numpy=
array([[0],
[0],
[0],
[0],
[1],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0]])>, 'restecg': <tf.Tensor: shape=(17, 1), dtype=int64, numpy=
array([[2],
[0],
[2],
[2],
[2],
[0],
[0],
[0],
[2],
[0],
[2],
[2],
[2],
[2],
[0],
[2],
[0]])>, 'thalach': <tf.Tensor: shape=(17, 1), dtype=int64, numpy=
array([[162],
[140],
[132],
[172],
[142],
[154],
[164],
[182],
[141],
[122],
[ 96],
[143],
[145],
[171],
[131],
[129],
[182]])>, 'exang': <tf.Tensor: shape=(17, 1), dtype=int64, numpy=
array([[1],
[0],
[1],
[0],
[1],
[1],
[1],
[0],
[1],
[0],
[1],
[0],
[0],
[0],
[0],
[1],
[0]])>, 'oldpeak': <tf.Tensor: shape=(17, 1), dtype=float64, numpy=
array([[0\. ],
[1.2],
[2.4],
[1.4],
[0.6],
[0\. ],
[0\. ],
[0\. ],
[2.8],
[2\. ],
[2.2],
[0\. ],
[6.2],
[0\. ],
[0.6],
[2.6],
[1.4]])>, 'slope': <tf.Tensor: shape=(17, 1), dtype=int64, numpy=
array([[1],
[2],
[2],
[1],
[2],
[1],
[1],
[1],
[2],
[2],
[3],
[1],
[3],
[1],
[2],
[2],
[1]])>, 'ca': <tf.Tensor: shape=(17, 1), dtype=int64, numpy=
array([[3],
[0],
[2],
[0],
[1],
[1],
[0],
[0],
[1],
[2],
[1],
[0],
[3],
[2],
[0],
[2],
[0]])>, 'thal': <tf.Tensor: shape=(17, 1), dtype=string, numpy=
array([[b'normal'],
[b'reversible'],
[b'reversible'],
[b'normal'],
[b'fixed'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'reversible'],
[b'normal'],
[b'reversible'],
[b'normal']], dtype=object)>}
Consider rewriting this model with the Functional API.
7/7 [==============================] - 0s 41ms/step - loss: 0.6719 - accuracy: 0.7098 - val_loss: 0.5845 - val_accuracy: 0.7347
<tensorflow.python.keras.callbacks.History at 0x7f8188a50eb8>
loss, accuracy = model.evaluate(test_ds)
print("Accuracy", accuracy)
WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor, but we receive a <class 'dict'> input: {'age': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[66],
[56],
[41],
[61],
[54],
[39],
[63],
[61],
[52],
[39],
[60],
[54],
[57],
[57],
[69],
[53],
[44],
[59],
[53],
[63],
[57],
[57],
[58],
[52],
[48],
[61],
[48],
[70],
[71],
[65],
[47],
[57]])>, 'sex': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[0],
[1],
[0],
[1],
[1],
[0],
[0],
[0],
[1],
[1],
[0],
[1],
[0],
[0],
[1],
[0],
[1],
[1],
[1],
[1],
[1],
[0],
[0],
[1],
[0],
[1],
[1],
[1],
[0],
[0],
[1],
[1]])>, 'cp': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[3],
[2],
[3],
[4],
[2],
[3],
[2],
[4],
[4],
[3],
[4],
[3],
[0],
[4],
[3],
[4],
[3],
[1],
[4],
[1],
[4],
[1],
[4],
[2],
[3],
[1],
[2],
[4],
[4],
[3],
[4],
[0]])>, 'trestbps': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[146],
[120],
[112],
[120],
[192],
[138],
[140],
[145],
[108],
[140],
[158],
[125],
[140],
[140],
[140],
[138],
[120],
[160],
[140],
[145],
[130],
[130],
[170],
[128],
[130],
[134],
[130],
[145],
[112],
[160],
[110],
[130]])>, 'chol': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[278],
[236],
[268],
[260],
[283],
[220],
[195],
[307],
[233],
[321],
[305],
[273],
[241],
[241],
[254],
[234],
[226],
[273],
[203],
[233],
[131],
[236],
[225],
[205],
[275],
[234],
[245],
[174],
[149],
[360],
[275],
[131]])>, 'fbs': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[1],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[1],
[1],
[0],
[0],
[1],
[1],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0]])>, 'restecg': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[2],
[0],
[2],
[0],
[2],
[0],
[0],
[2],
[0],
[2],
[2],
[2],
[1],
[0],
[2],
[2],
[0],
[2],
[2],
[2],
[0],
[0],
[2],
[0],
[0],
[0],
[2],
[0],
[0],
[2],
[2],
[1]])>, 'thalach': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[152],
[178],
[172],
[140],
[195],
[152],
[179],
[146],
[147],
[182],
[161],
[152],
[123],
[123],
[146],
[160],
[169],
[125],
[155],
[150],
[115],
[174],
[146],
[184],
[139],
[145],
[180],
[125],
[125],
[151],
[118],
[115]])>, 'exang': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[0],
[0],
[1],
[1],
[0],
[0],
[0],
[1],
[0],
[0],
[0],
[0],
[1],
[1],
[0],
[0],
[0],
[0],
[1],
[0],
[1],
[0],
[1],
[0],
[0],
[0],
[0],
[1],
[0],
[0],
[1],
[1]])>, 'oldpeak': <tf.Tensor: shape=(32, 1), dtype=float64, numpy=
array([[0\. ],
[0.8],
[0\. ],
[3.6],
[0\. ],
[0\. ],
[0\. ],
[1\. ],
[0.1],
[0\. ],
[0\. ],
[0.5],
[0.2],
[0.2],
[2\. ],
[0\. ],
[0\. ],
[0\. ],
[3.1],
[2.3],
[1.2],
[0\. ],
[2.8],
[0\. ],
[0.2],
[2.6],
[0.2],
[2.6],
[1.6],
[0.8],
[1\. ],
[1.2]])>, 'slope': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[2],
[1],
[1],
[2],
[1],
[2],
[1],
[2],
[1],
[1],
[1],
[3],
[1],
[2],
[2],
[1],
[1],
[1],
[3],
[3],
[2],
[1],
[2],
[1],
[1],
[2],
[2],
[3],
[2],
[1],
[2],
[1]])>, 'ca': <tf.Tensor: shape=(32, 1), dtype=int64, numpy=
array([[1],
[0],
[0],
[1],
[1],
[0],
[2],
[0],
[3],
[0],
[0],
[1],
[0],
[0],
[3],
[0],
[0],
[0],
[0],
[0],
[1],
[1],
[2],
[0],
[0],
[2],
[0],
[0],
[0],
[0],
[1],
[1]])>, 'thal': <tf.Tensor: shape=(32, 1), dtype=string, numpy=
array([[b'normal'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'fixed'],
[b'reversible'],
[b'2'],
[b'fixed'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'normal'],
[b'normal']], dtype=object)>}
Consider rewriting this model with the Functional API.
1/2 [==============>...............] - ETA: 0s - loss: 0.3588 - accuracy: 0.8125WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor, but we receive a <class 'dict'> input: {'age': <tf.Tensor: shape=(29, 1), dtype=int64, numpy=
array([[66],
[40],
[55],
[65],
[58],
[41],
[61],
[60],
[52],
[58],
[54],
[56],
[58],
[65],
[54],
[69],
[63],
[55],
[54],
[58],
[65],
[44],
[59],
[63],
[48],
[35],
[45],
[46],
[51]])>, 'sex': <tf.Tensor: shape=(29, 1), dtype=int64, numpy=
array([[0],
[1],
[0],
[1],
[0],
[1],
[1],
[0],
[0],
[1],
[0],
[1],
[1],
[0],
[0],
[0],
[0],
[1],
[0],
[1],
[0],
[1],
[1],
[0],
[1],
[1],
[1],
[0],
[0]])>, 'cp': <tf.Tensor: shape=(29, 1), dtype=int64, numpy=
array([[1],
[4],
[2],
[4],
[3],
[4],
[4],
[1],
[3],
[3],
[3],
[2],
[3],
[4],
[3],
[1],
[4],
[2],
[2],
[4],
[3],
[4],
[4],
[4],
[4],
[4],
[3],
[2],
[3]])>, 'trestbps': <tf.Tensor: shape=(29, 1), dtype=int64, numpy=
array([[150],
[152],
[135],
[135],
[120],
[110],
[140],
[150],
[136],
[112],
[160],
[130],
[140],
[150],
[108],
[140],
[150],
[130],
[132],
[146],
[155],
[110],
[170],
[108],
[130],
[120],
[110],
[105],
[120]])>, 'chol': <tf.Tensor: shape=(29, 1), dtype=int64, numpy=
array([[226],
[223],
[250],
[254],
[340],
[172],
[207],
[240],
[196],
[230],
[201],
[221],
[211],
[225],
[267],
[239],
[407],
[262],
[288],
[218],
[269],
[197],
[326],
[269],
[256],
[198],
[264],
[204],
[295]])>, 'fbs': <tf.Tensor: shape=(29, 1), dtype=int64, numpy=
array([[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[1],
[0],
[0],
[0],
[0],
[0],
[1],
[0],
[0],
[0],
[0],
[0],
[1],
[0],
[0],
[0],
[0]])>, 'restecg': <tf.Tensor: shape=(29, 1), dtype=int64, numpy=
array([[0],
[0],
[2],
[2],
[0],
[2],
[2],
[0],
[2],
[2],
[0],
[2],
[2],
[2],
[2],
[0],
[2],
[0],
[2],
[0],
[0],
[2],
[2],
[0],
[2],
[0],
[1],
[0],
[2]])>, 'thalach': <tf.Tensor: shape=(29, 1), dtype=int64, numpy=
array([[114],
[181],
[161],
[127],
[172],
[158],
[138],
[171],
[169],
[165],
[163],
[163],
[165],
[114],
[167],
[151],
[154],
[155],
[159],
[105],
[148],
[177],
[140],
[169],
[150],
[130],
[132],
[172],
[157]])>, 'exang': <tf.Tensor: shape=(29, 1), dtype=int64, numpy=
array([[0],
[0],
[0],
[0],
[0],
[0],
[1],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[1],
[0],
[0],
[0],
[1],
[1],
[1],
[1],
[0],
[0],
[0]])>, 'oldpeak': <tf.Tensor: shape=(29, 1), dtype=float64, numpy=
array([[2.6],
[0\. ],
[1.4],
[2.8],
[0\. ],
[0\. ],
[1.9],
[0.9],
[0.1],
[2.5],
[0\. ],
[0\. ],
[0\. ],
[1\. ],
[0\. ],
[1.8],
[4\. ],
[0\. ],
[0\. ],
[2\. ],
[0.8],
[0\. ],
[3.4],
[1.8],
[0\. ],
[1.6],
[1.2],
[0\. ],
[0.6]])>, 'slope': <tf.Tensor: shape=(29, 1), dtype=int64, numpy=
array([[3],
[1],
[2],
[2],
[1],
[1],
[1],
[1],
[2],
[2],
[1],
[1],
[1],
[2],
[1],
[1],
[2],
[1],
[1],
[2],
[1],
[1],
[3],
[2],
[1],
[2],
[1],
[1],
[1]])>, 'ca': <tf.Tensor: shape=(29, 1), dtype=int64, numpy=
array([[0],
[0],
[0],
[1],
[0],
[0],
[1],
[0],
[0],
[1],
[1],
[0],
[0],
[3],
[0],
[2],
[3],
[0],
[1],
[1],
[0],
[1],
[0],
[2],
[2],
[0],
[0],
[0],
[0]])>, 'thal': <tf.Tensor: shape=(29, 1), dtype=string, numpy=
array([[b'normal'],
[b'reversible'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'reversible'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'reversible'],
[b'normal'],
[b'reversible'],
[b'reversible'],
[b'normal'],
[b'normal'],
[b'normal']], dtype=object)>}
Consider rewriting this model with the Functional API.
2/2 [==============================] - 0s 14ms/step - loss: 0.4201 - accuracy: 0.8197
Accuracy 0.8196721076965332
关键点：通常使用更大更复杂的数据集进行深度学习，您将看到最佳结果。使用像这样的小数据集时，我们建议使用决策树或随机森林作为强有力的基准。本教程的目的不是训练一个准确的模型，而是演示处理结构化数据的机制，这样，在将来使用自己的数据集时，您有可以使用的代码作为起点。
下一步
了解有关分类结构化数据的更多信息的最佳方法是亲自尝试。我们建议寻找另一个可以使用的数据集，并使用和上面相似的代码，训练一个模型，对其分类。要提高准确率，请仔细考虑模型中包含哪些特征，以及如何表示这些特征。