python - Keras 2.0.8 仅使用 Python 3.x 执行 1 个 epoch，使用 2.x 执行 10 个

Question

如果我将它切换到 Python 2.x，它执行 10。这是为什么？

训练逻辑回归模型

                                 import keras.backend as K
    from keras.models            import Sequential
    from keras.layers            import Dense, Activation
    from keras.optimizers        import SGD
    from sklearn.model_selection import train_test_split, cross_val_score

    X_train, X_test, y_train, y_test = train_test_split(X, y,
                                                    test_size    = 0.3,
                                                    random_state = 42)

# NOTE: If I run this in Python 3.x, it only performs 1 Epoch
K.clear_session()

model = Sequential()
model.add(Dense(1, input_shape=(4,), activation='sigmoid'))

model.compile(loss     = 'binary_crossentropy',
              optimizer= 'sgd',
              metrics  = ['accuracy'])

# Saved the result of the fitting, to display the history as a data frame & see how the model does
history = model.fit     (X_train, y_train)
result  = model.evaluate(X_test,  y_test)

输出:

Epoch 1/10
960/960 [==============================] - 0s - loss: 0.7943 - acc: 0.5219     
Epoch 2/10
960/960 [==============================] - 0s - loss: 0.7338 - acc: 0.5469     
Epoch 3/10
960/960 [==============================] - 0s - loss: 0.6847 - acc: 0.5688     
Epoch 4/10
960/960 [==============================] - 0s - loss: 0.6446 - acc: 0.6177     
Epoch 5/10
960/960 [==============================] - 0s - loss: 0.6113 - acc: 0.6719     
Epoch 6/10
960/960 [==============================] - 0s - loss: 0.5832 - acc: 0.7000     
Epoch 7/10
960/960 [==============================] - 0s - loss: 0.5591 - acc: 0.7177     
Epoch 8/10
960/960 [==============================] - 0s - loss: 0.5381 - acc: 0.7365     
Epoch 9/10
960/960 [==============================] - 0s - loss: 0.5196 - acc: 0.7542     
Epoch 10/10
960/960 [==============================] - 0s - loss: 0.5031 - acc: 0.7688     
 32/412 [=>............................] - ETA: 0s

Answer 1

fit函数具有参数 epochs，默认值为 1。

fit(self, x=None, y=None, batch_size=None, epochs=1, verbose=1, callbacks=None, 
    validation_split=0.0, validation_data=None, shuffle=True, class_weight=None, 
    sample_weight=None, initial_epoch=0, steps_per_epoch=None, 
    validation_steps=None)

但是，默认值过去是 10。查看 this commit 中 models.py 中 fit 的更改例如。您很可能使用旧版本的 Keras 和 Python 2。