【631】TensorBoard 简介： TensorFlow 的可视化框架

 

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import keras from keras import layers from keras.datasets import imdb from keras.preprocessing import sequence   max_features = 2000 max_len = 500   (x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=max_features) x_train = sequence.pad_sequences(x_train, maxlen=max_len) x_test = sequence.pad_sequences(x_test, maxlen=max_len)   model = keras.models.Sequential() model.add(layers.Embedding(max_feature, 128,                           input_length=max_len,                           name='embed')) model.add(layers.Conv1D(32, 7, activation='relu')) model.add(layers.MaxPool1D(5)) model.add(layers.Conv1D(32, 7, activation='relu')) model.add(layers.GlobalMaxPool1D()) model.add(layers.Dense(1)) model.summary() model.compile(optimizer='rmsprop',              loss='binary_crossentropy',              metrics=['acc'])   callbacks = [     keras.callbacks.TensorBoard(         log_dir='my_log_dir',         histogram_freq=1,         embeddings_freq=1,     ) ]   history = model.fit(x_train, y_train,                    epochs=20,                    batch_size=128,                    validation_split=0.2,                    callbacks=callbacks)