VGG16_Keras_Treansferlearning

vgg16_keras_transferlearning.py

rom keras import applications
from keras import optimizers
from keras.models import Sequential, Model 
from keras.layers import Dropout, Flatten, Dense
from keras import backend as k 
from pprint import pprint as pp

img_width, img_height = 300, 300
train_data_dir = "/train"
validation_data_dir = "/validation"
nb_train_samples = 200
nb_validation_samples = 40 
batch_size = 20
epochs = 50

model = applications.VGG16(weights = "imagenet", include_top=False, input_shape = (img_width, img_height, 3))

# Freeze the layers which you don't want to train. Here I am freezing the first 10 layers.
for layer in model.layers:
    layer.trainable = False
    
#Adding custom Layers 
x = model.output
x = Flatten()(x)
x = Dense(1024, activation="relu")(x)
x = Dropout(0.5)(x)
x = Dense(1024, activation="relu")(x)
predictions = Dense(100, activation="softmax")(x)

# creating the final model 
model_final = Model(input = model.input, output = predictions)

# compile the model 
model_final.compile(loss = "categorical_crossentropy", 
                    optimizer = optimizers.SGD(lr=0.0001, momentum=0.9), 
                    metrics=["accuracy"])