def creat_jpg_dataset(self, data_path_list, data_size):
data_list = Dataset.list_files(data_path_list, shuffle = False)
dataset = data_list.map(self.path_process, num_parallel_calls = tf.data.experimental.AUTOTUNE)
dataset = dataset.shuffle(self.dataset_buffer_size).repeat()
dataset = dataset.batch(self.batch_size)
step_each_epoch = math.ceil(data_size / self.batch_size)
return dataset, step_each_epoch
def acc_test(resize_train_data_path, model_path, batch_size):
train_image_list = Dataset.list_files(resize_train_data_path + "*.jpg")
train_image_dataset = train_image_list.map(process_path, num_parallel_calls = tf.data.experimental.AUTOTUNE)
train_image_dataset = train_image_dataset.shuffle(62)
train_image_dataset = train_image_dataset.batch(batch_size)
steps_each_epoch = math.ceil(62 / batch_size)
cmodel = tf.keras.models.load_model(model_path + "c_model")
step = 0
acc_list = []
label = ['reality', 'animation']
train_image_dataset_iterator = iter(train_image_dataset)
while step < steps_each_epoch:
batch_label, batch_image = next(train_image_dataset_iterator)
batch_label = label_process(batch_label, label)
predict = tf.round(cmodel.predict(batch_image))
predict = tf.math.equal(batch_label, predict)
acc_list.append(tf.reduce_mean(tf.cast(predict, tf.float32)))
step += 1
acc_mean = np.mean(acc_list)
print(f"Acc: {acc_mean}")
training_classification = False
if training_classification:
cmodel_optimizer = tf.keras.optimizers.Adam()
#批次數量
batch_size = 1
image_count = 1
if first_training:
epochs = 5000
else:
epochs = 200
#匯入資料
if training_classification:
train_image_list = Dataset.list_files(resize_data_path + "*.jpg")
train_image_dataset = train_image_list.map(process_path, num_parallel_calls = tf.data.experimental.AUTOTUNE)
train_image_dataset = train_image_dataset.shuffle(62).repeat()
train_image_dataset = train_image_dataset.batch(batch_size)
steps_each_epoch = math.ceil(62 / batch_size)
else:
reality_image_list = Dataset.list_files(resize_reality_data_path + "/*.jpg")
reality_image_dataset = reality_image_list.map(process_path, num_parallel_calls = tf.data.experimental.AUTOTUNE)
reality_image_dataset = reality_image_dataset.shuffle(38).repeat()
reality_image_dataset = reality_image_dataset.batch(batch_size)
Animation_image_list = Dataset.list_files(resize_animation_data_path + "/*.jpg")
Animation_image_dataset = Animation_image_list.map(process_path, num_parallel_calls = tf.data.experimental.AUTOTUNE)
Animation_image_dataset = Animation_image_dataset.shuffle(38).repeat()
Animation_image_dataset = Animation_image_dataset.batch(batch_size)
return final_model
# %%
def process_path(file_path):
print(file_path)
file = read_file(file_path)
file = image.decode_jpeg(file, channels=3)
file = cast(file, float32)
file = preprocess_input(file)
file = image.resize(file, [ROW, COL])
return file
inference = Dataset.list_files(str(file_location + '/*'), shuffle=False)
inference = inference.map(process_path).batch(BATCH_SIZE).prefetch(AUTOTUNE)
# %%
checkpoints = glob.glob(os.path.join('Weights', 'IDP_Final_B*.h5'))
Models = []
for checkpoint in checkpoints:
new_model = create_model(training=False)
new_model.load_weights(checkpoint)
Models = new_model
print('Models loaded...')
sentence = ''
#匯入檔案
image = tf.io.read_file(file_path)
image = decode_image(image)
return file_name, image
#將影像轉為0~255的區間並改成uint8 data type
def deprocess(img):
img = 255 * (img + 1.0) / 2.0
return tf.cast(img, tf.uint8)
base_image_path = 'E:/python program/Deep dream test/*.jpg'
#base_image_path = 'E:/python program/dogs-vs-cats/test/*.jpg'
#根據給予的路徑建立一個包含所有符合條件檔案路徑的dataset
image_list = Dataset.list_files(base_image_path)
#Dataset.list_files 的輸出為帶有數個檔案路徑的eager_tensor
#for file in image_list.batch(2):
# print(file)
#根據給予的function 建立 Mapdataset物件
#會再被調用時將dataset中的元素帶入function
#num_parallel_calls 用來決定在載入多個元素時是否要用平行運算
image_dataset = image_list.map(
process_path, num_parallel_calls=tf.data.experimental.AUTOTUNE)
#for file_name, image in image_dataset.take(1):
# plt.imshow(image)
# plt.show()
