# -*- coding: utf-8 -*- """ Spyder Editor This is a temporary script file. python 3.6 pip install tensorflow-gpu==2.6.0 pip install keras==2.6.0 pip install opencv-python==3.4.2.16 pip install matplotlib==3.3.4 """ import numpy as np import os # import cv2 from sklearn.model_selection import train_test_split # pip install scikit-learn from tensorflow import keras from tensorflow.keras import layers from tensorflow.keras import models from tensorflow.keras.preprocessing.image import img_to_array from tensorflow.keras.preprocessing.image import load_img import tensorflow as tf import os class LearningRateTracker(keras.callbacks.Callback): def __init__(self): self._lr = list() def on_epoch_end(self, epoch, logs=None): optimizer = self.model.optimizer self._lr.append(tf.keras.backend.get_value(optimizer.lr)) print(f'Epoch {epoch + 1}: Learning rate is {tf.keras.backend.get_value(optimizer.lr)}.\n') def show_train_learningRate(self): plt.plot(self._lr) plt.title('learningRate') plt.ylabel('learningRate') plt.xlabel('Epoch') plt.legend(['train', 'validation'], loc='upper left') plt.savefig('LearningRate.png') # plt.show() # os.environ[''] # ============================================================================= from keras.utils import np_utils # ============================================================================= config = tf.compat.v1.ConfigProto() gpus = tf.config.experimental.list_physical_devices('GPU') os.environ["CUDA_VISIBLE_DEVICES"] = '0' session = tf.compat.v1.Session(config=config) epochs = 150 # 訓練的次數 batch_size = 18 img_rows = None # 驗證碼影像檔的高 img_cols = None # 驗證碼影像檔的寬 # digits_in_img = 6 #驗證碼影像檔中有幾位數 x_list = list() # 存所有驗證碼數字影像檔的array y_list = list() # 存所有的驗證碼數字影像檔array代表的正確數字 x_train = list() # 存訓練用驗證碼數字影像檔的array y_train = list() # 存訓練用驗證碼數字影像檔array代表的正確數字 x_test = list() # 存測試用驗證碼數字影像檔的array y_test = list() # 存測試用驗證碼數字影像檔array代表的正確數字 def split_digits_in_img(img_array, x_list, y_list=None): # 副函式：分割及儲存驗證碼 # for i in range(digits_in_img): # step = img_cols // digits_in_img #step=圖片總寬度除六 # y_list.append(img_filename[:2]) #將圖片正確數字(檔名)存進 y_list x_list.append(img_array) # 將圖片存進x_list input_filepath = r'D:\trainning\trainningCreat0321' file_list = sorted(os.listdir(input_filepath)) # 获取文件名列表. print(f'file_list = {file_list}') for classnumber in file_list: img_filenames = os.listdir(f'{input_filepath}/{classnumber}') count = 0 # os.listdir() 方法用于返回指定的文件夹包含的文件或文件夹的名字的列表 for img_filename in img_filenames: count += 1 if '.png' not in img_filename: # 只讀取PNG檔 continue # img = load_img('./output_file(00)/{0}/{1}'.format(classnumber, img_filename), color_mode='rgb', target_size=(224, 224, 3)) #將圖片依序以灰階讀取 img = load_img('{0}/{1}/{2}'.format(input_filepath, classnumber, img_filename), color_mode='grayscale', target_size=(112, 112, 1)) # 將圖片依序以灰階讀取 img_array = img_to_array(img) # 將圖片轉換成數值 img_rows, img_cols, _ = img_array.shape # 將圖片長寬分別儲存在img_rows, img_cols, # print(img_array.shape) y_list.append(classnumber[:2]) # 將圖片正確數字(檔名)存進 y_list split_digits_in_img(img_array, x_list, y_list) # 呼叫 副函式：分割及儲存驗證碼 print(f'{classnumber} is ok --> 有{count}張') x_train, x_test, y_train, y_test = train_test_split(x_list, y_list, test_size=0.001, stratify=y_list) # , stratify=y_list随机划分训练集和测试集 # print(x_train) # print(y_train) y_train = keras.utils.to_categorical(y_train) # 將y_list 轉換成categorical形式 y_test = keras.utils.to_categorical(y_test) # ============================================================================= # y_train_onehot = np_utils.to_categorical(y_train) # y_test_onehot = np_utils.to_categorical(y_test) # ============================================================================= # ============================================================================= # if os.path.isfile('cnn_model.h5'): #檢測是否已有模型 # model = models.load_model('cnn_model.h5') #將該模型導入 # print('Model loaded from file.') # else: #否則：建立新模型 # ============================================================================= model = models.Sequential() # 創建模型 model.add(layers.Conv2D(input_shape=(112, 112, 1), filters=64, kernel_size=(4, 4), padding="same", activation="relu")) model.add(layers.Conv2D(filters=64, kernel_size=(3, 3), padding="same", activation="relu")) model.add(layers.MaxPool2D(pool_size=(3, 3), strides=(2, 2))) # model.add(layers.Dropout(rate=0.1)) model.add(layers.Conv2D(filters=128, kernel_size=(3, 3), padding="same", activation="relu")) model.add(layers.Conv2D(filters=128, kernel_size=(3, 3), padding="same", activation="relu")) model.add(layers.MaxPool2D(pool_size=(3, 3), strides=(2, 2))) model.add(layers.Dropout(rate=0.6)) model.add(layers.Conv2D(filters=256, kernel_size=(3, 3), padding="same", activation="relu")) model.add(layers.Conv2D(filters=256, kernel_size=(3, 3), padding="same", activation="relu")) # ------------------------------------model.add(layers.Conv2D(filters=256, kernel_size=(3, 3), padding="same", activation="relu")) model.add(layers.MaxPool2D(pool_size=(3, 3), strides=(2, 2))) model.add(layers.Dropout(rate=0.8)) # model.add(layers.Conv2D(filters=512, kernel_size=(3, 3), padding="same", activation="relu")) model.add(layers.Conv2D(filters=512, kernel_size=(3, 3), padding="same", activation="relu")) # ----------------------------------------model.add(layers.Conv2D(filters=512, kernel_size=(3, 3), padding="same", activation="relu")) model.add(layers.MaxPool2D(pool_size=(3, 3), strides=(2, 2))) # model.add(layers.Dropout(rate=0.6)) # model.add(layers.Conv2D(filters=512, kernel_size=(3, 3), padding="same", activation="relu")) # model.add(layers.Conv2D(filters=512, kernel_size=(3, 3), padding="same", activation="relu")) # model.add(layers.Conv2D(filters=512, kernel_size=(3, 3), padding="same", activation="relu")) # model.add(layers.MaxPool2D(pool_size=(3, 3), strides=(2, 2))) model.add(layers.Flatten()) model.add(layers.Dense(units=4096, activation="relu")) model.add(layers.Dropout(rate=0.2)) model.add(layers.Dense(units=4096, activation="relu")) model.add(layers.Dense(units=17, activation="softmax")) # 這是類別數 # 全連接層 (輸出空間的維數,activation) print('New model created.') # 全連接層 (輸出空間的維數,activation) model.compile(loss=keras.losses.categorical_crossentropy, optimizer=keras.optimizers.Adam(learning_rate=0.00001, beta_1=0.9, beta_2=0.999), metrics=['accuracy']) # model.compile 編譯模型（optimizer =優化器，loss =損失函數， metrics= [“準確率”]） # model.fit(np.array(x_train), np.array(y_train), batch_size=digits_in_img, epochs=epochs, verbose=1, validation_data=(np.array(x_test), np.array(y_test))) # ============================================================================= checkpoint_filepath = os.path.sep.join(['checkpoint', "weights-{epoch:03d}-{loss:.9f}-{accuracy:.6f}.h5"]) model_checkpoint_callback = tf.keras.callbacks.ModelCheckpoint( filepath=checkpoint_filepath, save_weights_only=False, monitor='loss', mode='min', save_best_only=True) checkpoint_filepath = os.path.sep.join(['checkpoint_accuracy', "weights-{epoch:03d}-{loss:.9f}-{accuracy:.6f}.h5"]) model_checkpoint_callback_accuracy = tf.keras.callbacks.ModelCheckpoint( filepath=checkpoint_filepath, save_weights_only=False, monitor='accuracy', mode='max', save_best_only=True) lr_tracker = LearningRateTracker() with tf.device('/cpu:0'): x_train = tf.convert_to_tensor(x_train, np.float32) y_train = tf.convert_to_tensor(y_train, np.float32) train_history = model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, verbose=1, callbacks=[model_checkpoint_callback, model_checkpoint_callback_accuracy, lr_tracker], validation_data=(np.array(x_test), np.array(y_test)), shuffle=True) import matplotlib.pyplot as plt def show_train_history(train_history, title, train_metric, val_metric): plt.plot(train_history.history[train_metric]) plt.plot(train_history.history[val_metric]) plt.title(title) plt.ylabel(train_metric) plt.xlabel('Epoch') plt.legend(['train', 'validation'], loc='upper left') plt.savefig(f'{title}.png') # plt.show() show_train_history(train_history, 'Accuracy', 'accuracy', 'val_accuracy') show_train_history(train_history, 'Loss', 'loss', 'val_loss') lr_tracker.show_train_learningRate() # ============================================================================= loss, accuracy = model.evaluate(np.array(x_test), np.array(y_test), verbose=1) # 評估模型(verbose：0或1。詳細模式。 0 =靜音，1 =進度欄。) # print('Test loss:', loss) # print('Test accuracy:', accuracy) model.save('model0325_4060.h5') print(model.summary()) # # -*- coding: utf-8 -*- # """ # Spyder Editor # # This is a temporary script file. # # # python 3.6 # pip install tensorflow-gpu==2.6.0 # pip install keras==2.6.0 # pip install opencv-python==3.4.2.16 # pip install matplotlib==3.3.4 # # """ # import numpy as np # import os # # import cv2 # from sklearn.model_selection import train_test_split # pip install scikit-learn # from tensorflow import keras # from tensorflow.keras import layers # from tensorflow.keras import models # from tensorflow.keras.preprocessing.image import img_to_array # from tensorflow.keras.preprocessing.image import load_img # import tensorflow as tf # import os # # # class LearningRateTracker(keras.callbacks.Callback): # def __init__(self): # self._lr = list() # # def on_epoch_end(self, epoch, logs=None): # optimizer = self.model.optimizer # self._lr.append(tf.keras.backend.get_value(optimizer.lr)) # print(f'Epoch {epoch + 1}: Learning rate is {tf.keras.backend.get_value(optimizer.lr)}.\n') # # def show_train_learningRate(self): # plt.plot(self._lr) # plt.title('learningRate') # plt.ylabel('learningRate') # plt.xlabel('Epoch') # plt.legend(['train', 'validation'], loc='upper left') # plt.savefig('LearningRate.png') # plt.show() # # # # os.environ[''] # # ============================================================================= # from keras.utils import np_utils # # # ============================================================================= # config = tf.compat.v1.ConfigProto() # gpus = tf.config.experimental.list_physical_devices('GPU') # os.environ["CUDA_VISIBLE_DEVICES"] = '0' # session = tf.compat.v1.Session(config=config) # # epochs = 150 # 訓練的次數 # batch_size = 16 # img_rows = None # 驗證碼影像檔的高 # img_cols = None # 驗證碼影像檔的寬 # # digits_in_img = 6 #驗證碼影像檔中有幾位數 # x_list = list() # 存所有驗證碼數字影像檔的array # y_list = list() # 存所有的驗證碼數字影像檔array代表的正確數字 # x_train = list() # 存訓練用驗證碼數字影像檔的array # y_train = list() # 存訓練用驗證碼數字影像檔array代表的正確數字 # x_test = list() # 存測試用驗證碼數字影像檔的array # y_test = list() # 存測試用驗證碼數字影像檔array代表的正確數字 # # # def split_digits_in_img(img_array, x_list, y_list=None): # 副函式：分割及儲存驗證碼 # # for i in range(digits_in_img): # # step = img_cols // digits_in_img #step=圖片總寬度除六 # # y_list.append(img_filename[:2]) #將圖片正確數字(檔名)存進 y_list # x_list.append(img_array) # 將圖片存進x_list # # # input_filepath = './trainningset/0920 Milwaukee_TrainCreat' # file_list = sorted(os.listdir(input_filepath)) # 获取文件名列表. # print(f'file_list = {file_list}') # for classnumber in file_list: # img_filenames = os.listdir(f'{input_filepath}/{classnumber}') # count = 0 # os.listdir() 方法用于返回指定的文件夹包含的文件或文件夹的名字的列表 # for img_filename in img_filenames: # count += 1 # if '.png' not in img_filename: # 只讀取PNG檔 # continue # # img = load_img('./output_file(00)/{0}/{1}'.format(classnumber, img_filename), color_mode='rgb', target_size=(224, 224, 3)) #將圖片依序以灰階讀取 # img = load_img('{0}/{1}/{2}'.format(input_filepath, classnumber, img_filename), color_mode='grayscale', # target_size=(224, 224, 1)) # 將圖片依序以灰階讀取 # img_array = img_to_array(img) # 將圖片轉換成數值 # img_rows, img_cols, _ = img_array.shape # 將圖片長寬分別儲存在img_rows, img_cols, # # print(img_array.shape) # y_list.append(classnumber[:2]) # 將圖片正確數字(檔名)存進 y_list # split_digits_in_img(img_array, x_list, y_list) # 呼叫 副函式：分割及儲存驗證碼 # print(f'{classnumber} is ok --> 有{count}張') # x_train, x_test, y_train, y_test = train_test_split(x_list, y_list, test_size=0.1, # stratify=y_list) # , stratify=y_list随机划分训练集和测试集 # # print(x_train) # # print(y_train) # # y_train = keras.utils.to_categorical(y_train) # 將y_list 轉換成categorical形式 # y_test = keras.utils.to_categorical(y_test) # # # ============================================================================= # # y_train_onehot = np_utils.to_categorical(y_train) # # y_test_onehot = np_utils.to_categorical(y_test) # # ============================================================================= # # # ============================================================================= # # if os.path.isfile('cnn_model.h5'): #檢測是否已有模型 # # model = models.load_model('cnn_model.h5') #將該模型導入 # # print('Model loaded from file.') # # else: #否則：建立新模型 # # ============================================================================= # # # model = models.Sequential() # # 創建模型 # model.add(layers.Conv2D(input_shape=(224, 224, 1), filters=64, kernel_size=(4, 4), padding="same", activation="relu")) # model.add(layers.Conv2D(filters=64, kernel_size=(3, 3), padding="same", activation="relu")) # model.add(layers.MaxPool2D(pool_size=(3, 3), strides=(2, 2))) # # model.add(layers.Dropout(rate=0.1)) # model.add(layers.Conv2D(filters=128, kernel_size=(3, 3), padding="same", activation="relu")) # model.add(layers.Conv2D(filters=128, kernel_size=(3, 3), padding="same", activation="relu")) # model.add(layers.MaxPool2D(pool_size=(3, 3), strides=(2, 2))) # # model.add(layers.Dropout(rate=0.2)) # model.add(layers.Conv2D(filters=256, kernel_size=(3, 3), padding="same", activation="relu")) # model.add(layers.Conv2D(filters=256, kernel_size=(3, 3), padding="same", activation="relu")) # model.add(layers.Conv2D(filters=256, kernel_size=(3, 3), padding="same", activation="relu")) # model.add(layers.MaxPool2D(pool_size=(3, 3), strides=(2, 2))) # # model.add(layers.Dropout(rate=0.7)) # model.add(layers.Conv2D(filters=512, kernel_size=(3, 3), padding="same", activation="relu")) # model.add(layers.Conv2D(filters=512, kernel_size=(3, 3), padding="same", activation="relu")) # model.add(layers.Conv2D(filters=512, kernel_size=(3, 3), padding="same", activation="relu")) # model.add(layers.MaxPool2D(pool_size=(3, 3), strides=(2, 2))) # model.add(layers.Dropout(rate=0.8)) # model.add(layers.Conv2D(filters=512, kernel_size=(3, 3), padding="same", activation="relu")) # model.add(layers.Conv2D(filters=512, kernel_size=(3, 3), padding="same", activation="relu")) # model.add(layers.Conv2D(filters=512, kernel_size=(3, 3), padding="same", activation="relu")) # model.add(layers.MaxPool2D(pool_size=(3, 3), strides=(2, 2))) # # model.add(layers.Flatten()) # model.add(layers.Dense(units=4096, activation="relu")) # model.add(layers.Dropout(rate=0.6)) # model.add(layers.Dense(units=4096, activation="relu")) # model.add(layers.Dense(units=16, activation="softmax")) # 這是類別數 # # 全連接層 (輸出空間的維數,activation) # print('New model created.') # 全連接層 (輸出空間的維數,activation) # # model.compile(loss=keras.losses.categorical_crossentropy, # optimizer=keras.optimizers.Adam(learning_rate=0.00001, beta_1=0.9, beta_2=0.999), metrics=['accuracy']) # # model.compile 編譯模型（optimizer =優化器，loss =損失函數， metrics= [“準確率”]） # # model.fit(np.array(x_train), np.array(y_train), batch_size=digits_in_img, epochs=epochs, verbose=1, validation_data=(np.array(x_test), np.array(y_test))) # # ============================================================================= # checkpoint_filepath = os.path.sep.join(['checkpoint', # "weights-{epoch:03d}-{loss:.9f}-{val_accuracy:.4f}.h5"]) # model_checkpoint_callback = tf.keras.callbacks.ModelCheckpoint( # filepath=checkpoint_filepath, # save_weights_only=False, # monitor='loss', # mode='min', # save_best_only=True) # # lr_tracker = LearningRateTracker() # train_history = model.fit(np.array(x_train), np.array(y_train), batch_size=batch_size, epochs=epochs, verbose=1, # callbacks=[model_checkpoint_callback, lr_tracker], # validation_data=(np.array(x_test), np.array(y_test)), shuffle=True) # import matplotlib.pyplot as plt # # # def show_train_history(train_history, title, train_metric, val_metric): # plt.plot(train_history.history[train_metric]) # plt.plot(train_history.history[val_metric]) # plt.title(title) # plt.ylabel(train_metric) # plt.xlabel('Epoch') # plt.legend(['train', 'validation'], loc='upper left') # plt.savefig(f'{title}.png') # plt.show() # # # show_train_history(train_history, 'Accuracy', 'accuracy', 'val_accuracy') # show_train_history(train_history, 'Loss', 'loss', 'val_loss') # lr_tracker.show_train_learningRate() # # ============================================================================= # # loss, accuracy = model.evaluate(np.array(x_test), np.array(y_test), verbose=1) # 評估模型(verbose：0或1。詳細模式。 0 =靜音，1 =進度欄。) # # print('Test loss:', loss) # # print('Test accuracy:', accuracy) # # model.save('model1002.h5') # print(model.summary())