getting the weights of intermediate layer in keras
The easiest way to create a truncated output from a network is create a sub-network of it and apply weights of your trained network. The following example is a modification of what you have shown up there, but it will guide you out

Network you want to train originally


model = Sequential()
model.add(Conv2D(10, (3, 3), padding='same', input_shape=(60, 60, 3),
                 activation="relu"))
model.add(Conv2D(10, (3, 3), activation="relu"))
model.add(MaxPooling2D(pool_size=(3, 3)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(5, activation="softmax"))
model.compile(
    loss='categorical_crossentropy',
    optimizer='adam',
    metrics=['accuracy'])

model.fit(
    train_data,
    train_label)


Now create a subnetwork from which you want the outputs, like from above example


model_new = Sequential()
model_new.add(Conv2D(10, (3, 3), padding='same', input_shape=(60, 60, 3),
                     activation="relu"))
model_new.add(Conv2D(10, (3, 3), activation="relu"))
model_new.add(MaxPooling2D(pool_size=(3, 3)))
model_new.add(Dropout(0.25))
model_new.add(Flatten())

model_new.compile(
    loss='categorical_crossentropy',
    optimizer='adam',
    metrics=['mse'])

# You need to apply fit on random array's created, just so as to initialise 
# weights. Anyways you will replacing them with original ones from above.
model_new.fit(train_data, y=np.random.rand(40, 3610))


Now take weights from the first trained network and replace the weights of the second network like


model_new.set_weights(weights=model.get_weights())


You can check whether the weights are changed in the above step by actually adding these check statements like


print("Are arrays equal before fit - ",
      any([np.array_equal(a1, a2) for a1, a2 in zip(model_new.get_weights(), model.get_weights()[:4])]))

model_new.set_weights(weights=model.get_weights())
print("Are arrays equal after applying weights - ",
      all([np.array_equal(a1, a2) for a1, a2 in zip(model_new.get_weights(), model.get_weights()[:4])]))


This should yeild


Are arrays equal before fit -  False
Are arrays equal after applying weights -  True


Hope this helps.