I'm trying to visualize the output of each convolutional layer in keras, following this link: MNIST Visualisation. I have modified some layers to remove errors, but now I'm stuck with the Dense Layer Error.
np.set_printoptions(precision=5, suppress=True)
np.random.seed(1337) # for reproducibility
nb_classes = 10
# the data, shuffled and split between tran and test sets
(X_train, y_train), (X_test, y_test) = mnist.load_data("mnist.pkl")
X_train = X_train.reshape(X_train.shape[0], 1, 28, 28)
X_test = X_test.reshape(X_test.shape[0], 1, 28, 28)
X_train = X_train.astype("float32")
X_test = X_test.astype("float32")
X_train /= 255
X_test /= 255
print('X_train shape:', X_train.shape)
print(X_train.shape[0], 'train samples')
print(X_test.shape[0], 'test samples')
# convert class vectors to binary class matrices
Y_train = np_utils.to_categorical(y_train, nb_classes)
Y_test = np_utils.to_categorical(y_test, nb_classes)
i = 4600
pl.imshow(X_train[i, 0], interpolation='nearest', cmap=cm.binary)
print("label : ", Y_train[i,:])
model = Sequential()
model.add(Convolution2D(32, 3, 3, border_mode='same',input_shape = (1,28,28))) #changed border_mode from full -> valid
convout1 = Activation('relu')
model.add(convout1)
model.add(Convolution2D(32, 32, 3))
convout2 = Activation('relu')
model.add(convout2)
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(32*196, 128)) #ERROR HERE
Any comment or suggestion highly appreciated. Thank you.
If you check the documentation of a Dense layer then you'll notice that the first argument it accepts is the shape of output and second is init which describes the way how weights of the layer are initiated. In your case you provided the int as second positional argument and this caused error. You should change the code to (assuming that you want an output in a form of 128-dimensional vector):
model.add(Dense(128))