3692109a392c5f90829bae24133b58d113b0eab2,examples/convolutional_model.py,,,#,2

Before Change

print("Processing data...")
imageProcessor = ImageProcessor(image_data, target_dimensions=target_dimensions, rgb=False, channels=1)
image_array = imageProcessor.process_training_data()
image_data = np.array([[image] for image in image_array])
if verbose:
	print ("processed image data shape: " + str(image_data.shape))

print("Creating training/testing data...")

After Change

print("Creating training/testing data...")
validation_split = 0.15
X_train, X_test, y_train, y_test = train_test_split(image_data, labels,
                                                    test_size=validation_split, random_state=42, stratify=labels)


train_gen = DataGenerator().fit(X_train, y_train)
test_gen = DataGenerator().fit(X_test, y_test)
print("Training net...")
model = ConvolutionalNN(target_dimensions, channels, label_count)
model.fit_generator(train_gen.generate(batch_size=10), test_gen.generate(batch_size=10), epochs=10)

Instances