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from keras.applications import VGG19 | |
from keras.layers import * | |
from keras import Sequential | |
### Parameters | |
# learning_rate = 0.0001 | |
# decay_speed = 1e-6 | |
# momentum = 0.09 | |
# loss_function = "sparse_categorical_crossentropy" | |
source_model = VGG19(weights=None) | |
#new_layer = Dense(num_classes, activation=activations.softmax, name='prediction') | |
drop_layer = Dropout(0.5) | |
drop_layer2 = Dropout(0.5) | |
model = Sequential() | |
for layer in source_model.layers[:-1]: # go through until last layer | |
if layer == source_model.layers[-25]: | |
model.add(BatchNormalization()) | |
model.add(layer) | |
# if layer == source_model.layers[-3]: | |
# model.add(drop_layer) | |
# model.add(drop_layer2) | |
model.add(Dense(num_classes, activation="softmax")) | |
model.summary() | |
opt1 = keras.optimizers.RMSprop(learning_rate = 0.0001, momentum = 0.09) | |
opt2 = keras.optimizers.Adam(learning_rate=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-07) | |
model.compile(optimizer=opt1, | |
loss="sparse_categorical_crossentropy", | |
metrics=["accuracy"]) | |
#sgd = SGD(lr=learning_rate, decay=decay_speed, momentum=momentum, nesterov=True) | |
# rms = keras.optimizers.RMSprop(lr=learning_rate, momentum=momentum) | |
# model.compile(optimizer=rms, | |
# loss=loss_function, | |
# metrics=["accuracy"]) | |
# print("Model compiled! \n") |
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