feature_columns.py

ShadowEditor.AI/README.md

@@ -33,6 +33,7 @@ pip install tensorflow-gpu==2.0.0-rc0
 4. tensorflow/mnist_expert.py: 专家级手写数字识别，准确度：98.112%
 5. tensorflow/image_classification.py: 服装图片分类，准确度：87.81%
 6. tensorflow/basic_text_classification.py: 评论文本分类，准确度：86.2%
+7. tensorflow/feature_columns.py: 对结构化数据进行分类，准确度：72.54%
 
 ## 相关地址
 

ShadowEditor.AI/tensorflow/feature_columns.py

@@ -0,0 +1,134 @@
+# https://tensorflow.google.cn/beta/tutorials/keras/feature_columns
+
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+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
+
+URL = 'https://storage.googleapis.com/applied-dl/heart.csv'
+dataframe = pd.read_csv(URL)
+dataframe.head()
+
+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')
+
+# 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)
+
+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)
+
+# 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())
+
+
+age = feature_column.numeric_column("age")
+demo(age)
+
+age_buckets = feature_column.bucketized_column(
+    age, boundaries=[18, 25, 30, 35, 40, 45, 50, 55, 60, 65])
+demo(age_buckets)
+
+thal = feature_column.categorical_column_with_vocabulary_list(
+    'thal', ['fixed', 'normal', 'reversible'])
+
+thal_one_hot = feature_column.indicator_column(thal)
+demo(thal_one_hot)
+
+# 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)
+
+thal_hashed = feature_column.categorical_column_with_hash_bucket(
+    'thal', hash_bucket_size=1000)
+demo(feature_column.indicator_column(thal_hashed))
+
+crossed_feature = feature_column.crossed_column(
+    [age_buckets, thal], hash_bucket_size=1000)
+demo(feature_column.indicator_column(crossed_feature))
+
+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)
+
+feature_layer = tf.keras.layers.DenseFeatures(feature_columns)
+
+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)
+
+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)
+
+loss, accuracy = model.evaluate(test_ds)
+print("Accuracy", accuracy)