Deep Neural Networks
简单的神经网络
from tensorflow import keras
from tensorflow.keras import layers
model = keras.Sequential([
layers.Dense(1024, activation='relu', input_shape=[11]),
layers.Dropout(0.3),
layers.BatchNormalization(),
layers.Dense(1024, activation='relu'),
layers.Dropout(0.3),
layers.BatchNormalization(),
layers.Dense(1024, activation='relu'),
layers.Dropout(0.3),
layers.BatchNormalization(),
layers.Dense(1),
])
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添加损失和优化器
model.compile(
optimizer="adam",
loss="mae",
)
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定义 EarlyStopping
from tensorflow.keras.callbacks import EarlyStopping
early_stopping = EarlyStopping(
min_delta=0.001, # minimium amount of change to count as an improvement
patience=20, # how many epochs to wait before stopping
restore_best_weights=True,
)
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训练
history = model.fit(
X_train, y_train,
validation_data=(X_valid, y_valid),
batch_size=256,
epochs=500,
callbacks=[early_stopping], # put your callbacks in a list
verbose=0, # turn off training log
)
history_df = pd.DataFrame(history.history)
history_df.loc[:, ['loss', 'val_loss']].plot();
print("Minimum validation loss: {}".format(history_df['val_loss'].min()))
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