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Commit 44cc0f86 authored by Picon Ruiz, Artzai's avatar Picon Ruiz, Artzai
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first final version

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......@@ -43,7 +43,7 @@ def generate_simple_cnn_regression_model(input_shape,n_blocks=2,weights='',is_re
model.load_weights(weights,by_name=True)
if freeze:
for layer in model.layers[:-1]:
for layer in model.layers[:-2]:
layer.trainable = False
return model
......
......@@ -39,7 +39,7 @@ if __name__ == "__main__":
model_data = generate_simple_regression_model(input_shape_data, weights='regression_model_data.h5',remove_head=True)
model_img = generate_simple_cnn_regression_model(input_shape_img, weights='regression_model_image_pretrained.h5',remove_head=False)
model_img = generate_simple_cnn_regression_model(input_shape_img, weights='regression_model_image_pretrained.h5',remove_head=True)
input_data = [Input(input_shape_data),Input((input_shape_img,input_shape_img,3))]
# y_data = model_data.layers[-2]#(input_data[0])
......@@ -48,18 +48,19 @@ if __name__ == "__main__":
y_img = model_img(input_data[1])
y = Concatenate()([y_data, y_img])
y = Dense(32, activation='relu')(y)
y = Dense(1,activation='sigmoid')(y)
for layer in model_img.layers:
layer.trainable = False
# for layer in model_img.layers:
# layer.trainable = False
model = Model(input_data,y)
opt = Adam(lr=1e-3, decay=1e-3 / 200)
model.compile(loss='mean_squared_error',
opt = Adam(lr=1e-3, decay=1e-3/400)
model.compile(loss='mean_absolute_error',
metrics=['mean_absolute_percentage_error', 'mean_absolute_error', 'mean_squared_error'],
optimizer=opt)
model.summary()
model = train_model(trainX, trainY, testX, testY, model, show_plot=True, epochs=500, batch_size=32)
model = train_model(trainX, trainY, testX, testY, model, show_plot=True, epochs=400, batch_size=32)
evaluate_regression_model(model, testX, testY, normalizer, show_plot=True)
model.save('regression_model_combined.h5')
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