diff --git a/house_prices_estimation_example_data.py b/house_prices_estimation_example_data.py
index daffbba509de3e1e74ba6dc45622978931b0bb0e..c51f2cc4d49f76f8488d1cd444c47e58528d9bf8 100644
--- a/house_prices_estimation_example_data.py
+++ b/house_prices_estimation_example_data.py
@@ -1,7 +1,7 @@
# -*- coding: utf-8 -*-
-
+from tensorflow_core.python.keras.engine.input_layer import Input
from tensorflow_core.python.keras.layers.core import Dense, Dropout
-from tensorflow_core.python.keras.models import Sequential
+from tensorflow_core.python.keras.models import Sequential, Model
from tensorflow_core.python.keras.optimizer_v2.adam import Adam
from datasets import load_house_dataset_data
@@ -10,13 +10,21 @@ import matplotlib.pyplot as plt
__author__ = 106360
-def generate_simple_regression_model(input_shape):
- model = Sequential()
- model.add(Dense(16, input_dim=input_shape, activation="relu"))
- # model.add(Dropout(0.25))
- model.add(Dense(6, activation="relu"))
- # model.add(Dropout(0.25))
- model.add(Dense(1, activation="sigmoid"))
+def generate_simple_regression_model(input_shape,weights='',remove_head=False):
+ # define the model input
+ inputs = Input(shape=(input_shape))
+ # loop over the number of filters
+
+ x = Dense(16, input_dim=input_shape, activation="relu", name='layer1')(inputs)
+ x = Dense(6, activation="relu",name='layer2')(x)
+
+ if not remove_head:
+ x= Dense(1, activation="sigmoid",name='layer3')(x)
+
+ model = Model(inputs,x)
+ if weights!='':
+ model.load_weights(weights,by_name=True)
+
return model
diff --git a/house_prices_estimation_example_image.py b/house_prices_estimation_example_image.py
index a2d1343d80d2cc52f3b865e053cb9f11e1957ded..24fa7098833b48ecfe12948f0f96d40bd7d94b71 100644
--- a/house_prices_estimation_example_image.py
+++ b/house_prices_estimation_example_image.py
@@ -11,13 +11,13 @@ from tensorflow_core.python.keras.models import Sequential, Model, load_model
from tensorflow_core.python.keras.optimizer_v2.adam import Adam
import tensorflow as tf
-from train_and_evaluation import evaluate_regression_model
+from train_and_evaluation import evaluate_regression_model, train_model
from datasets import load_house_dataset_data, DatasetType
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):
+def generate_simple_cnn_regression_model(input_shape,n_blocks=2,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
@@ -29,12 +29,14 @@ def generate_simple_cnn_regression_model(input_shape,n_blocks=2,weights='',is_re
x = MaxPooling2D()(x)
x= Flatten()(x)
-
- if is_regression:
- y = Dense(num_classes, activation="sigmoid",name='last_dense_reg')(x)
+ x = Dense(6, 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)
+ else:
+ y = Dense(num_classes, activation="softmax", name='last_dense_clf')(x)
else:
- y = Dense(num_classes, activation="softmax", name='last_dense_clf')(x)
-
+ y=x
model = Model(inputs,y)
if weights!='':
@@ -115,7 +117,7 @@ def train_cifar100(num_classes=100,batch_size=32):
if __name__ == "__main__":
- pre_train_with_cifar100 = False
+ pre_train_with_cifar100 = True
weights = ''
if pre_train_with_cifar100:
file_weight_cifar100 = 'pretrained_cifar100.h5'
diff --git a/house_prices_estimation_example_image_and_data.py b/house_prices_estimation_example_image_and_data.py
index 88cd8f7f69bac2be33fe9304eb63fcc25c93b5f6..98b6da53a10aa37be3517ea1d22014b8a2c5eb25 100644
--- a/house_prices_estimation_example_image_and_data.py
+++ b/house_prices_estimation_example_image_and_data.py
@@ -1,4 +1,11 @@
# -*- coding: utf-8 -*-
+from tensorflow_core.python.keras.layers.merge import Concatenate
+
+
+from house_prices_estimation_example_data import generate_simple_regression_model
+from house_prices_estimation_example_image import generate_simple_cnn_regression_model
+
+__author__ = 106360
import os
from tensorflow_core.python.keras.datasets import cifar10
@@ -11,80 +18,43 @@ from tensorflow_core.python.keras.models import Sequential, Model, load_model
from tensorflow_core.python.keras.optimizer_v2.adam import Adam
import tensorflow as tf
-from utils import plot_regression, load_house_dataset_data, DatasetType
+from datasets import load_house_dataset_data, DatasetType
import matplotlib.pyplot as plt
-__author__ = 106360
-
-
-def train_model(trainX, trainY, testX, testY, model, epochs=200, batch_size=16,show_plot=True):
- history = model.fit(trainX, trainY, validation_data=(testX, testY),
- epochs=epochs, batch_size=batch_size)
+from train_and_evaluation import train_model, evaluate_regression_model
- if show_plot:
- acc = history.history['mean_absolute_percentage_error']
- val_acc = history.history['val_mean_absolute_percentage_error']
- loss = history.history['loss']
- val_loss = history.history['val_loss']
- epochs_range = range(epochs)
- plt.figure(figsize=(8, 8))
- plt.subplot(1, 2, 1)
- plt.plot(epochs_range, acc, label='Training mean_absolute_percentage_error')
- plt.plot(epochs_range, val_acc, label='Validation mean_absolute_percentage_error')
- plt.legend(loc='upper right')
- plt.title('Training and Validation \% error')
-
- plt.subplot(1, 2, 2)
- plt.plot(epochs_range, loss, label='Training Loss')
- plt.plot(epochs_range, val_loss, label='Validation Loss')
- plt.legend(loc='upper right')
- plt.title('Training and Validation Loss')
- plt.show()
-
- return model
+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)
-if __name__ == "__main__":
- pre_train_with_cifar100 = False
- weights = ''
- if pre_train_with_cifar100:
- file_weight_cifar100 = 'pretrained_cifar100.h5'
+ trainX = [trainX_data,trainX_img['frontal_img']]
+ testX = [testX_data,testX_img['frontal_img']]
+ input_shape_data = trainX[0].shape[1]
+ input_shape_img = trainX[1].shape[1]
- try:
- model = load_model(file_weight_cifar100)
- except:
- model = train_cifar100()
- model.save(file_weight_cifar100)
- weights = file_weight_cifar100
- (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)
+ 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)
+ input_data = [Input(input_shape_data),Input((input_shape_img,input_shape_img,3))]
+ # y_data = model_data.layers[-2]#(input_data[0])
+ # y_img = model_img.layers[-2]#(input_data[1])
+ y_data = model_data(input_data[0])
+ y_img = model_img(input_data[1])
- trainX = trainX_img['frontal_img']
- testX = testX_img['frontal_img']
- input_shape = trainX.shape[1]
+ y = Concatenate()([y_data, y_img])
+ y = Dense(1,activation='sigmoid')(y)
- if pre_train_with_cifar100:
- file_weight_finetune = 'regression_model_image_finetune.h5'
- model = generate_simple_cnn_regression_model(input_shape,weights=weights,freeze=True)
- opt = Adam(lr=1e-3, decay=1e-3 / 200)
- model.compile(loss='mean_squared_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)
- evaluate_regression_model(model,testX,testY,normalizer,show_plot=True)
- model.save(file_weight_finetune)
- weights = file_weight_finetune
- final_model_weight = 'regression_model_image_pretrained.h5'
- else:
- final_model_weight = 'regression_model_image_from_scratch.h5'
+ for layer in model_img.layers:
+ layer.trainable = False
+ model = Model(input_data,y)
- model = generate_simple_cnn_regression_model(input_shape, weights=weights)
opt = Adam(lr=1e-3, decay=1e-3 / 200)
model.compile(loss='mean_squared_error',
metrics=['mean_absolute_percentage_error', 'mean_absolute_error', 'mean_squared_error'],
@@ -92,4 +62,4 @@ if __name__ == "__main__":
model.summary()
model = train_model(trainX, trainY, testX, testY, model, show_plot=True, epochs=500, batch_size=32)
evaluate_regression_model(model, testX, testY, normalizer, show_plot=True)
- model.save(final_model_weight)
+ model.save('regression_model_combined.h5')