another version

house_prices_estimation_example_image.py

@@ -17,19 +17,19 @@ import matplotlib.pyplot as plt
 
 __author__ = 106360
 
-def generate_simple_cnn_regression_model(input_shape,n_blocks=2,weights='',is_regression=True,num_classes=1,freeze=False,remove_head=False):
+def generate_simple_cnn_regression_model(input_shape,n_blocks=3,weights='',is_regression=True,num_classes=1,freeze=False,remove_head=False):
     # define the model input
     inputs = Input(shape=(input_shape,input_shape,3))
     # loop over the number of filters
     x = inputs
     for n in range(n_blocks):
-        x = Conv2D(16, (3, 3), padding="same",name='conv_%d' % n)(x)
+        x = Conv2D(32, (3, 3), padding="same",name='conv_%d' % n)(x)
         x = Activation("relu")(x)
-        x = BatchNormalization()(x)
+        # x = BatchNormalization()(x)
         x = MaxPooling2D()(x)
 
     x= Flatten()(x)
-    x = Dense(6, activation="relu", name='pre_last_dense_reg')(x)
+    x = Dense(16, activation="relu", name='pre_last_dense_reg')(x)
     if not remove_head:
         if is_regression:
             y = Dense(num_classes, activation="sigmoid",name='last_dense_reg')(x)

house_prices_estimation_example_image_and_data.py

@@ -31,15 +31,15 @@ if __name__ == "__main__":
     (trainX_data,trainX_img, trainY, testX_data,testX_img,testY), normalizer = load_house_dataset_data(test_size=0.2,random_state=666,type=DatasetType.Both)
 
 
-    trainX = [trainX_data,trainX_img['frontal_img']]
-    testX = [testX_data,testX_img['frontal_img']]
+    trainX = [trainX_data,trainX_img['bathroom_img']]
+    testX = [testX_data,testX_img['bathroom_img']]
     input_shape_data = trainX[0].shape[1]
     input_shape_img = trainX[1].shape[1]
 
 
 
     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=True)
+    model_img = generate_simple_cnn_regression_model(input_shape_img, weights='regression_model_image_pretrained.h5',remove_head=False)
     input_data = [Input(input_shape_data),Input((input_shape_img,input_shape_img,3))]
 
     # y_data = model_data.layers[-2]#(input_data[0])
@@ -48,7 +48,8 @@ if __name__ == "__main__":
     y_img = model_img(input_data[1])
 
     y = Concatenate()([y_data, y_img])
-    y = Dense(32, activation='relu')(y)
+    y = BatchNormalization()(y)
+    y = Dense(16, activation='relu')(y)
     y = Dense(1,activation='sigmoid')(y)
 
     # for layer in model_img.layers:
@@ -56,11 +57,11 @@ if __name__ == "__main__":
     model = Model(input_data,y)
 
 
-    opt = Adam(lr=1e-3, decay=1e-3/400)
+    opt = Adam(lr=1e-3, decay=1e-3/250)
     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=400, batch_size=32)
+    model = train_model(trainX, trainY, testX, testY, model, show_plot=True, epochs=2000, batch_size=32)
     evaluate_regression_model(model, testX, testY, normalizer, show_plot=True)
     model.save('regression_model_combined.h5')