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Data Science Tensorflow Handbook

Use when working with TensorFlow — deep learning, Keras, neural networks, ML models

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TensorFlow 实践手册

Keras API

序列模型定义

from tensorflow.keras import Sequential
from tensorflow.keras.layers import Dense

model = Sequential([
    Dense(64, activation='relu', input_shape=(784,)),
    Dense(64, activation='relu'),
    Dense(10, activation='softmax')
])

model.compile(
    optimizer='adam',
    loss='sparse_categorical_crossentropy',
    metrics=['accuracy']
)

分布式训练

多GPU训练配置

strategy = tf.distribute.MirroredStrategy()

with strategy.scope():
    model = create_model()
    model.compile(loss='sparse_categorical_crossentropy',
                 optimizer='adam',
                 metrics=['accuracy'])

model.fit(train_dataset, epochs=10)

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