![]() ![]() Print('Using real-time data augmentation. Validation_data=(dataset.X_valid, dataset.Y_valid), Self.test_image_generator.fit(self.X_train)ĭef train(self, dataset, batch_size=32, nb_epoch=40, data_augmentation=True): Self.test_image_generator = ImageDataGenerator(**generator_kwargs) Self.image_generator = ImageDataGenerator(**generator_kwargs, **train_generator_kwargs) Super(TinyDatasetGenerator, self)._init_() train_generator_kwargs: Dictionary with keyword arguments passed to Keras' ImageDataGenerator for the training set. generator_kwargs: Dictionary with keyword arguments passed to Keras' ImageDataGenerator for both training and test. y_test: list with numeric labels for the test images. y_train: list with numeric labels for the training images. X_test: 4-D numpy array with the test images. X_train: 4-D numpy array with the training images. Since Linux uses 'fork(2)' to spawn child processes and the initialization of the data generator was happening outside of the MP part, all workers were using the same seed and were generating equal batches. """ Abstract base class for interfaces to datasets with low-resolution images that fit entirely into memory (e.g., CIFAR). Turns out the problem was a combination of OS behavior and my data generator, which was internally doing some shuffling using np.random. # Put all images in sample_images/test folder Model.fit_generator(train_generator, epochs=400, pile(optimizer=optimizers.RMSprop(lr=1e-5), Train_generator = datagen.flow(train_x, train_y, sample_weight=sample_weight)īase = VGG16(weights='imagenet', include_top=False, input_shape=(None, None, 3)) (train_x, train_y, sample_weight), (test_x, test_y) = load_data()ĭatagen = ImageDataGenerator(horizontal_flip=True, Model.fit_generator(generated_data.flow(X, Y, batch_size=8), steps_per_epoch=X.shape//8, epochs=25, validation_data=(X_test, Y_test), callbacks=checkpoints) Generated_data = ImageDataGenerator(featurewise_center=False, samplewise_center=False, featurewise_std_normalization=False, samplewise_std_normalization=False, zca_whitening=False, rotation_range=0, width_shift_range=0.1, height_shift_range=0.1, horizontal_flip = True, vertical_flip = False) # model.fit(X, Y, batch_size=10, epochs=25, validation_data=(X_test, Y_test), shuffle=True, callbacks=checkpoints)įrom import ImageDataGenerator The duration of the training is extended. If not os.path.exists('Data/Checkpoints/'):Ĭheckpoints.append(ModelCheckpoint('Data/Checkpoints/best_weights.h5', monitor='val_loss', verbose=0, save_best_only=True, save_weights_only=True, mode='auto', period=1))Ĭheckpoints.append(TensorBoard(log_dir='Data/Checkpoints/./logs', histogram_freq=0, write_graph=True, write_images=False, embeddings_freq=0, embeddings_layer_names=None, embeddings_metadata=None)) Def train_model(model, X, X_test, Y, Y_test):
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |