From 44cc0f865a1a77414dc08fe96bda2afb9bfe3bc4 Mon Sep 17 00:00:00 2001
From: artzai <artzai.picon@tecnalia.com>
Date: Fri, 30 Aug 2019 13:21:14 +0200
Subject: [PATCH] first final version
---
house_prices_estimation_example_image.py | 2 +-
house_prices_estimation_example_image_and_data.py | 13 +++++++------
2 files changed, 8 insertions(+), 7 deletions(-)
diff --git a/house_prices_estimation_example_image.py b/house_prices_estimation_example_image.py
index 24fa709..46c76ea 100644
--- a/house_prices_estimation_example_image.py
+++ b/house_prices_estimation_example_image.py
@@ -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
diff --git a/house_prices_estimation_example_image_and_data.py b/house_prices_estimation_example_image_and_data.py
index 98b6da5..7760c2e 100644
--- a/house_prices_estimation_example_image_and_data.py
+++ b/house_prices_estimation_example_image_and_data.py
@@ -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')
--
GitLab