How to add and remove new layers in keras after loading weights?
I am trying to do a transfer learning; for that purpose I want to remove the last two layers of the neural network and add another two layers. This is an example code which also output the same error.
from keras.models import Sequential
from keras.layers import Input,Flatten
from keras.layers.convolutional import Convolution2D, MaxPooling2D
from keras.layers.core import Dropout, Activation
from keras.layers.pooling import GlobalAveragePooling2D
from keras.models import Model
in_img = Input(shape=(3, 32, 32))
x = Convolution2D(12, 3, 3, subsample=(2, 2), border_mode='valid', name='conv1')(in_img)
x = Activation('relu', name='relu_conv1')(x)
x = MaxPooling2D(pool_size=(3, 3), strides=(2, 2), name='pool1')(x)
x = Convolution2D(3, 1, 1, border_mode='valid', name='conv2')(x)
x = Activation('relu', name='relu_conv2')(x)
x = GlobalAveragePooling2D()(x)
o = Activation('softmax', name='loss')(x)
model = Model(input=in_img, output=[o])
model.compile(loss="categorical_crossentropy", optimizer="adam")
#model.load_weights('model_weights.h5', by_name=True)
model.summary()
model.layers.pop()
model.layers.pop()
model.summary()
model.add(MaxPooling2D())
model.add(Activation('sigmoid', name='loss'))
I removed the layer using pop() but when I tried to add its outputting this error
AttributeError: 'Model' object has no attribute 'add'
I know the most probable reason for the error is improper use of model.add(). what other syntax should I use?
EDIT:
I tried to remove/add layers in keras but its not allowing it to be added after loading external weights.
from keras.models import Sequential
from keras.layers import Input,Flatten
from keras.layers.convolutional import Convolution2D, MaxPooling2D
from keras.layers.core import Dropout, Activation
from keras.layers.pooling import GlobalAveragePooling2D
from keras.models import Model
in_img = Input(shape=(3, 32, 32))
def gen_model():
in_img = Input(shape=(3, 32, 32))
x = Convolution2D(12, 3, 3, subsample=(2, 2), border_mode='valid', name='conv1')(in_img)
x = Activation('relu', name='relu_conv1')(x)
x = MaxPooling2D(pool_size=(3, 3), strides=(2, 2), name='pool1')(x)
x = Convolution2D(3, 1, 1, border_mode='valid', name='conv2')(x)
x = Activation('relu', name='relu_conv2')(x)
x = GlobalAveragePooling2D()(x)
o = Activation('softmax', name='loss')(x)
model = Model(input=in_img, output=[o])
return model
#parent model
model=gen_model()
model.compile(loss="categorical_crossentropy", optimizer="adam")
model.summary()
#saving model weights
model.save('model_weights.h5')
#loading weights to second model
model2=gen_model()
model2.compile(loss="categorical_crossentropy", optimizer="adam")
model2.load_weights('model_weights.h5', by_name=True)
model2.layers.pop()
model2.layers.pop()
model2.summary()
#editing layers in the second model and saving as third model
x = MaxPooling2D()(model2.layers[-1].output)
o = Activation('sigmoid', name='loss')(x)
model3 = Model(input=in_img, output=[o])
its showing this error
RuntimeError: Graph disconnected: cannot obtain value for tensor input_4 at layer "input_4". The following previous layers were accessed without issue: []
You can take the output of the last model and create a new model. The lower layers remains the same.
model.summary()
model.layers.pop()
model.layers.pop()
model.summary()
x = MaxPooling2D()(model.layers[-1].output)
o = Activation('sigmoid', name='loss')(x)
model2 = Model(input=in_img, output=[o])
model2.summary()
Check How to use models from keras.applications for transfer learnig?
Update on Edit:
The new error is because you are trying to create the new model on global in_img which is actually not used in the previous model creation.. there you are actually defining a local in_img. So the global in_img is obviously not connected to the upper layers in the symbolic graph. And it has nothing to do with loading weights.
To better resolve this problem you should instead use model.input to reference to the input.
model3 = Model(input=model2.input, output=[o])
Another way to do it
from keras.models import Model
layer_name = 'relu_conv2'
model2= Model(inputs=model1.input, outputs=model1.get_layer(layer_name).output)
As of Keras 2.3.1 and TensorFlow 2.0, model.layers.pop() is not working as intended (see issue here). They suggested two options to do this.
One option is to recreate the model and copy the layers. For instance, if you want to remove the last layer and add another one, you can do:
model = Sequential()
for layer in source_model.layers[:-1]: # go through until last layer
model.add(layer)
model.add(Dense(3, activation='softmax'))
model.summary()
model.compile(optimizer='adam', loss='categorical_crossentropy')
Another option is to use the functional model:
predictions = Dense(3, activation='softmax')(source_model.layers[-2].output)
model = Model(inputs=inputs, outputs=predictions)
model.compile(optimizer='adam', loss='categorical_crossentropy')
model.layers[-1].output means the last layer's output which is the final output, so in your code, you actually didn't remove any layers, you added another head/path.
An alternative to Wesam Na's answer, if you don't know the layer names you can simply cut off the last layer via:
from keras.models import Model
model2= Model(inputs=model1.input, outputs=model1.layers[-2].output)
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