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135 lines
4.2 KiB
Python
135 lines
4.2 KiB
Python
# https://tensorflow.google.cn/beta/tutorials/keras/feature_columns
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from __future__ import absolute_import, division, print_function, unicode_literals
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import numpy as np
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import pandas as pd
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import tensorflow as tf
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from tensorflow import feature_column
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from tensorflow.keras import layers
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from sklearn.model_selection import train_test_split
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URL = 'https://storage.googleapis.com/applied-dl/heart.csv'
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dataframe = pd.read_csv(URL)
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dataframe.head()
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train, test = train_test_split(dataframe, test_size=0.2)
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train, val = train_test_split(train, test_size=0.2)
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print(len(train), 'train examples')
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print(len(val), 'validation examples')
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print(len(test), 'test examples')
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# 一种从 Pandas Dataframe 创建 tf.data 数据集的实用程序方法（utility method）
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def df_to_dataset(dataframe, shuffle=True, batch_size=32):
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dataframe = dataframe.copy()
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labels = dataframe.pop('target')
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ds = tf.data.Dataset.from_tensor_slices((dict(dataframe), labels))
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if shuffle:
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ds = ds.shuffle(buffer_size=len(dataframe))
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ds = ds.batch(batch_size)
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return ds
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batch_size = 5 # 小批量大小用于演示
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train_ds = df_to_dataset(train, batch_size=batch_size)
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val_ds = df_to_dataset(val, shuffle=False, batch_size=batch_size)
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test_ds = df_to_dataset(test, shuffle=False, batch_size=batch_size)
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for feature_batch, label_batch in train_ds.take(1):
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print('Every feature:', list(feature_batch.keys()))
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print('A batch of ages:', feature_batch['age'])
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print('A batch of targets:', label_batch)
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# 我们将使用该批数据演示几种特征列
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example_batch = next(iter(train_ds))[0]
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# 用于创建一个特征列
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# 并转换一批次数据的一个实用程序方法
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def demo(feature_column):
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feature_layer = layers.DenseFeatures(feature_column)
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print(feature_layer(example_batch).numpy())
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age = feature_column.numeric_column("age")
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demo(age)
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age_buckets = feature_column.bucketized_column(
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age, boundaries=[18, 25, 30, 35, 40, 45, 50, 55, 60, 65])
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demo(age_buckets)
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thal = feature_column.categorical_column_with_vocabulary_list(
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'thal', ['fixed', 'normal', 'reversible'])
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thal_one_hot = feature_column.indicator_column(thal)
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demo(thal_one_hot)
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# 注意到嵌入列的输入是我们之前创建的类别列
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thal_embedding = feature_column.embedding_column(thal, dimension=8)
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demo(thal_embedding)
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thal_hashed = feature_column.categorical_column_with_hash_bucket(
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'thal', hash_bucket_size=1000)
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demo(feature_column.indicator_column(thal_hashed))
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crossed_feature = feature_column.crossed_column(
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[age_buckets, thal], hash_bucket_size=1000)
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demo(feature_column.indicator_column(crossed_feature))
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feature_columns = []
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# 数值列
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for header in ['age', 'trestbps', 'chol', 'thalach', 'oldpeak', 'slope', 'ca']:
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feature_columns.append(feature_column.numeric_column(header))
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# 分桶列
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age_buckets = feature_column.bucketized_column(
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age, boundaries=[18, 25, 30, 35, 40, 45, 50, 55, 60, 65])
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feature_columns.append(age_buckets)
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# 分类列
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thal = feature_column.categorical_column_with_vocabulary_list(
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'thal', ['fixed', 'normal', 'reversible'])
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thal_one_hot = feature_column.indicator_column(thal)
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feature_columns.append(thal_one_hot)
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# 嵌入列
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thal_embedding = feature_column.embedding_column(thal, dimension=8)
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feature_columns.append(thal_embedding)
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# 组合列
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crossed_feature = feature_column.crossed_column(
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[age_buckets, thal], hash_bucket_size=1000)
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crossed_feature = feature_column.indicator_column(crossed_feature)
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feature_columns.append(crossed_feature)
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feature_layer = tf.keras.layers.DenseFeatures(feature_columns)
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batch_size = 32
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train_ds = df_to_dataset(train, batch_size=batch_size)
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val_ds = df_to_dataset(val, shuffle=False, batch_size=batch_size)
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test_ds = df_to_dataset(test, shuffle=False, batch_size=batch_size)
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model = tf.keras.Sequential([
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feature_layer,
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layers.Dense(128, activation='relu'),
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layers.Dense(128, activation='relu'),
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layers.Dense(1, activation='sigmoid')
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])
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model.compile(optimizer='adam',
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loss='binary_crossentropy',
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metrics=['accuracy'],
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run_eagerly=True)
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model.fit(train_ds,
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validation_data=val_ds,
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epochs=5)
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loss, accuracy = model.evaluate(test_ds)
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print("Accuracy", accuracy)
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