keras_intermediate_outputs.py

input = Input(shape=(2,))

probs = Dense(2, activation='softmax', name='probs')(input)
probs = Dropout(1e-100)(probs)

model = Model(input=input, output=probs)
model.compile(optimizer='sgd', loss='categorical_crossentropy', metrics=['accuracy'])

from keras.utils.np_utils import to_categorical

X, y = np.array([[1, 2], [3, 4]]), to_categorical([1, 0])

model.fit(X, y, validation_data=[X, y])

probas = model.get_layer('probs')

import keras.backend as K

f = K.function(inputs=model.inputs + [K.learning_phase()], outputs=[probas.output])

# test mode = 0, train mode = 1
f(inputs=[X, 1])