Train with custom weights

Question

Currently I am using transfer learning to train a neural network. I am using the ResNet50 pretrained model provided by keras.

base_model=ResNet50(weights='imagenet', include_top=False, input_shape=(224, 224, 3))

# function to finetune model
def build_finetune_model(base_model, dropout, fc_layers, num_classes):
    for layer in base_model.layers:
        layer.trainable = False

    x = base_model.output
    x = Flatten()(x)
    for fc in fc_layers:
        # New FC layer, random init
        x = Dense(fc, use_bias=False)(x)
        x = BatchNormalization()(x)
        x = Activation('relu')(x)
        x = Dropout(dropout)(x)

    # New softmax layer
    x = Dense(num_classes, use_bias=False)(x) 
    x = BatchNormalization()(x)
    predictions = Activation('softmax')(x)
    finetune_model = Model(inputs=base_model.input, outputs=predictions)

    return finetune_model

FC_LAYERS = [1024, 512]
dropout = 0.5

model = build_finetune_model(base_model, dropout=dropout, fc_layers=FC_LAYERS,num_classes=len(categories))

Now I wanted to see whether or not using Resnet50 1by2 (amongst others) would increase my accuracy. These models are provided as caffe models. I used the Caffe weight converter to convert these models to a keras h5 file.

The problem now is that these files do not contain a model that can be trained, only the weights. How can I use the weights to train a model in keras?

Answer 1

If you only have saved weights, you can only load those weights into a network with the same architecture. Assuming the weights you have match the architecture of the Keras Applications model, you can:

base_model = ResNet50(...,weights=None)
base_model.load_weights('my_weights_file.h5')
for layer in base_model.layers:
   layer.training = False