deep learning - Obtaining output of an Intermediate layer in TensorFlow/Keras

Question

I'm trying to obtain output of an intermediate layer in Keras, Following is my code:

XX = model.input # Keras Sequential() model object
YY = model.layers[0].output
F = K.function([XX], [YY]) # K refers to keras.backend


Xaug = X_train[:9]
Xresult = F([Xaug.astype('float32')])

Running this, I got an Error :

InvalidArgumentError (see above for traceback): You must feed a value for placeholder tensor 'dropout_1/keras_learning_phase' with dtype bool

i came to know that because I'm using dropout layer in my model, I have to specify a learning_phase() flag to my function as per keras documentation. I changed my code to the following:

XX = model.input
YY = model.layers[0].output
F = K.function([XX, K.learning_phase()], [YY])


Xaug = X_train[:9]
Xresult = F([Xaug.astype('float32'), 0])

Now I'm getting a new Error that I'm unable to figure out:

TypeError: Cannot interpret feed_dict key as Tensor: Can not convert a int into a Tensor.

Any help would be appreciated.
PS : I'm new to TensorFlow and Keras.

Edit 1 : Following is the complete code that I'm using. I'm using Spatial Transformer Network as discussed in this NIPS paper and it's Kera's implementation here

input_shape =  X_train.shape[1:]

# initial weights
b = np.zeros((2, 3), dtype='float32')
b[0, 0] = 1
b[1, 1] = 1
W = np.zeros((100, 6), dtype='float32')
weights = [W, b.flatten()]

locnet = Sequential()
locnet.add(Convolution2D(64, (3, 3), input_shape=input_shape, padding='same'))
locnet.add(Activation('relu'))
locnet.add(Convolution2D(64, (3, 3), padding='same'))
locnet.add(Activation('relu'))
locnet.add(MaxPooling2D(pool_size=(2, 2)))
locnet.add(Convolution2D(128, (3, 3), padding='same'))
locnet.add(Activation('relu'))
locnet.add(Convolution2D(128, (3, 3), padding='same'))
locnet.add(Activation('relu'))
locnet.add(MaxPooling2D(pool_size=(2, 2)))
locnet.add(Convolution2D(256, (3, 3), padding='same'))
locnet.add(Activation('relu'))
locnet.add(Convolution2D(256, (3, 3), padding='same'))
locnet.add(Activation('relu'))
locnet.add(MaxPooling2D(pool_size=(2, 2)))
locnet.add(Dropout(0.5))
locnet.add(Flatten())
locnet.add(Dense(100))
locnet.add(Activation('relu'))
locnet.add(Dense(6, weights=weights))


model = Sequential()

model.add(SpatialTransformer(localization_net=locnet,
                             output_size=(128, 128), input_shape=input_shape))

model.add(Convolution2D(64, (3, 3), padding='same'))
model.add(Activation('relu'))
model.add(Convolution2D(64, (3, 3), padding='same'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Convolution2D(128, (3, 3), padding='same'))
model.add(Activation('relu'))
model.add(Convolution2D(128, (3, 3), padding='same'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Convolution2D(256, (3, 3), padding='same'))
model.add(Activation('relu'))
model.add(Convolution2D(256, (3, 3), padding='same'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Convolution2D(256, (3, 3), padding='same'))
model.add(Activation('relu'))
model.add(Convolution2D(256, (3, 3), padding='same'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.5))
model.add(Flatten())
model.add(Dense(256))
model.add(Activation('relu'))

model.add(Dense(num_classes))
model.add(Activation('softmax'))

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

#==============================================================================
# Start Training
#==============================================================================
#define training results logger callback
csv_logger = keras.callbacks.CSVLogger(training_logs_path+'.csv')
model.fit(X_train, y_train,
          batch_size=batch_size,
          epochs=20,
          validation_data=(X_valid, y_valid),
          shuffle=True,
          callbacks=[SaveModelCallback(), csv_logger])




#==============================================================================
# Visualize what Transformer layer has learned
#==============================================================================

XX = model.input
YY = model.layers[0].output
F = K.function([XX, K.learning_phase()], [YY])


Xaug = X_train[:9]
Xresult = F([Xaug.astype('float32'), 0])

# input
for i in range(9):
    plt.subplot(3, 3, i+1)
    plt.imshow(np.squeeze(Xaug[i]))
    plt.axis('off')

for i in range(9):
    plt.subplot(3, 3, i + 1)
    plt.imshow(np.squeeze(Xresult[0][i]))
    plt.axis('off')

Answer 1

The easiest way is to create a new model in Keras, without calling the backend. You'll need the functional model API for this:

from keras.models import Model

XX = model.input 
YY = model.layers[0].output
new_model = Model(XX, YY)

Xaug = X_train[:9]
Xresult = new_model.predict(Xaug)