High loss (~7) after dropout modification

Part 1 (2017)
1

Hey guys,

i struggle since two days with my performance after i adjusted my dropout. I used the Function of class3 to perform the adjustment:

def get_fc_model(prev_p, new_p, conv_layers):
    model = Sequential([
        MaxPooling2D(input_shape=conv_layers[-1].output_shape[1:]),
        Flatten(),
        Dense(4096, activation='relu'),
        Dropout(new_p),
        Dense(4096, activation='relu'),
        Dropout(new_p),
        Dense(2, activation='softmax')
        ])
    
    for l in model.layers:
        if type(l)==Dense:
            l.set_weights(adj_wgts(l, prev_p, new_p))
            
    return model

def adj_wgts(layer, prev_p, new_p):
    scal = (1-prev_p)/(1-new_p)
    return [o*scal for o in layer.get_weights()]

After that i proceeded as followed:

model = vgg.model
layers = model.layers
last_conv_idx = [index for index, layer in enumerate(layers)
                            if type(layer) is Convolution2D][-1]
conv_layers = layers[:last_conv_idx+1]
conv_model = Sequential(conv_layers)
fc_s3 = get_fc_model(0.5, 0.65, conv_layers)
conv_model.add(fc_s3)

Since my own set could need some adjustment of the last convLayer i made this one trainable as well.
for layer in ft_s3.layers[:-2]: layer.trainable = False

i then compiled my model with a slow lr and started training.
For better comparison i used the cats and dogs as well and start with a loss of 7. after around 80 epocs my loss is at around 1.

Is there something i’m doing wrong?

cheers!