386 lines
18 KiB
Python
386 lines
18 KiB
Python
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# -*- coding: utf-8 -*-
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"""
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Spyder Editor
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This is a temporary script file.
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python 3.6
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pip install tensorflow-gpu==2.6.0
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pip install keras==2.6.0
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pip install opencv-python==3.4.2.16
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pip install matplotlib==3.3.4
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"""
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import numpy as np
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import os
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# import cv2
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from sklearn.model_selection import train_test_split # pip install scikit-learn
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from tensorflow import keras
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from tensorflow.keras import layers
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from tensorflow.keras import models
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from tensorflow.keras.preprocessing.image import img_to_array
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from tensorflow.keras.preprocessing.image import load_img
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import tensorflow as tf
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import os
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class LearningRateTracker(keras.callbacks.Callback):
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def __init__(self):
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self._lr = list()
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def on_epoch_end(self, epoch, logs=None):
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optimizer = self.model.optimizer
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self._lr.append(tf.keras.backend.get_value(optimizer.lr))
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print(f'Epoch {epoch + 1}: Learning rate is {tf.keras.backend.get_value(optimizer.lr)}.\n')
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def show_train_learningRate(self):
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plt.plot(self._lr)
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plt.title('learningRate')
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plt.ylabel('learningRate')
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plt.xlabel('Epoch')
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plt.legend(['train', 'validation'], loc='upper left')
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plt.savefig('LearningRate.png')
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# plt.show()
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# os.environ['']
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# =============================================================================
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from keras.utils import np_utils
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# =============================================================================
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config = tf.compat.v1.ConfigProto()
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gpus = tf.config.experimental.list_physical_devices('GPU')
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os.environ["CUDA_VISIBLE_DEVICES"] = '0'
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session = tf.compat.v1.Session(config=config)
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epochs = 150 # 訓練的次數
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batch_size = 18
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img_rows = None # 驗證碼影像檔的高
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img_cols = None # 驗證碼影像檔的寬
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# digits_in_img = 6 #驗證碼影像檔中有幾位數
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x_list = list() # 存所有驗證碼數字影像檔的array
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y_list = list() # 存所有的驗證碼數字影像檔array代表的正確數字
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x_train = list() # 存訓練用驗證碼數字影像檔的array
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y_train = list() # 存訓練用驗證碼數字影像檔array代表的正確數字
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x_test = list() # 存測試用驗證碼數字影像檔的array
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y_test = list() # 存測試用驗證碼數字影像檔array代表的正確數字
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def split_digits_in_img(img_array, x_list, y_list=None): # 副函式：分割及儲存驗證碼
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# for i in range(digits_in_img):
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# step = img_cols // digits_in_img #step=圖片總寬度除六
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# y_list.append(img_filename[:2]) #將圖片正確數字(檔名)存進 y_list
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x_list.append(img_array) # 將圖片存進x_list
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input_filepath = r'D:\ScrewdriverData\0504_train_Milwaukee\creat'
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file_list = sorted(os.listdir(input_filepath)) # 获取文件名列表.
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print(f'file_list = {file_list}')
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for classnumber in file_list:
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img_filenames = os.listdir(f'{input_filepath}/{classnumber}')
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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
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for img_filename in img_filenames:
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count += 1
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if '.png' not in img_filename: # 只讀取PNG檔
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continue
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# 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
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img = load_img('{0}/{1}/{2}'.format(input_filepath, classnumber, img_filename), color_mode='grayscale',
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target_size=(112, 112, 1)) # 將圖片依序以灰階讀取
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img_array = img_to_array(img) # 將圖片轉換成數值
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img_rows, img_cols, _ = img_array.shape # 將圖片長寬分別儲存在img_rows, img_cols,
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# print(img_array.shape)
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y_list.append(classnumber[:2]) # 將圖片正確數字(檔名)存進 y_list
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split_digits_in_img(img_array, x_list, y_list) # 呼叫 副函式：分割及儲存驗證碼
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print(f'{classnumber} is ok --> 有{count}張')
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x_train, x_test, y_train, y_test = train_test_split(x_list, y_list, test_size=0.001,
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stratify=y_list) # , stratify=y_list随机划分训练集和测试集
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# print(x_train)
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# print(y_train)
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y_train = keras.utils.to_categorical(y_train) # 將y_list 轉換成categorical形式
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y_test = keras.utils.to_categorical(y_test)
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# =============================================================================
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# y_train_onehot = np_utils.to_categorical(y_train)
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# y_test_onehot = np_utils.to_categorical(y_test)
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# =============================================================================
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# =============================================================================
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# if os.path.isfile('cnn_model.h5'): #檢測是否已有模型
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# model = models.load_model('cnn_model.h5') #將該模型導入
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# print('Model loaded from file.')
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# else: #否則：建立新模型
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# =============================================================================
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model = models.Sequential()
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model.add(layers.Conv2D(input_shape=(112, 112, 1), filters=64, kernel_size=(4, 4), padding="same", activation="relu"))
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model.add(layers.Conv2D(filters=64, kernel_size=(3, 3), padding="same", activation="relu"))
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model.add(layers.MaxPool2D(pool_size=(3, 3), strides=(2, 2)))
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model.add(layers.Conv2D(filters=128, kernel_size=(3, 3), padding="same", activation="relu"))
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model.add(layers.Conv2D(filters=128, kernel_size=(3, 3), padding="same", activation="relu"))
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model.add(layers.MaxPool2D(pool_size=(3, 3), strides=(2, 2)))
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model.add(layers.Conv2D(filters=256, kernel_size=(3, 3), padding="same", activation="relu"))
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model.add(layers.MaxPool2D(pool_size=(3, 3), strides=(2, 2)))
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model.add(layers.Flatten())
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model.add(layers.Dense(units=512, activation="relu"))
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model.add(layers.Dropout(rate=0.5))
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model.add(layers.Dense(units=128, activation="relu"))
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model.add(layers.Dense(units=18, activation="softmax"))# 這是類別數
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# 全連接層 (輸出空間的維數,activation)
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print('New model created.') # 全連接層 (輸出空間的維數,activation)
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model.compile(loss=keras.losses.categorical_crossentropy,
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optimizer=keras.optimizers.Adam(learning_rate=0.00001, beta_1=0.9, beta_2=0.999), metrics=['accuracy'])
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# model.compile 編譯模型（optimizer =優化器，loss =損失函數， metrics= [“準確率”]）
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# 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)))
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# =============================================================================
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checkpoint_filepath = os.path.sep.join(['checkpoint',
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"weights-{epoch:03d}-{loss:.9f}-{accuracy:.6f}.h5"])
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model_checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(
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filepath=checkpoint_filepath,
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save_weights_only=False,
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monitor='loss',
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mode='min',
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save_best_only=True)
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checkpoint_filepath = os.path.sep.join(['checkpoint_accuracy',
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"weights-{epoch:03d}-{loss:.9f}-{accuracy:.6f}.h5"])
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model_checkpoint_callback_accuracy = tf.keras.callbacks.ModelCheckpoint(
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filepath=checkpoint_filepath,
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save_weights_only=False,
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monitor='accuracy',
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mode='max',
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save_best_only=True)
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lr_tracker = LearningRateTracker()
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with tf.device('/cpu:0'):
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x_train = tf.convert_to_tensor(x_train, np.float32)
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y_train = tf.convert_to_tensor(y_train, np.float32)
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train_history = model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, verbose=1,
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callbacks=[model_checkpoint_callback, model_checkpoint_callback_accuracy, lr_tracker],
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validation_data=(np.array(x_test), np.array(y_test)), shuffle=True)
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import matplotlib.pyplot as plt
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def show_train_history(train_history, title, train_metric, val_metric):
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plt.plot(train_history.history[train_metric])
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plt.plot(train_history.history[val_metric])
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plt.title(title)
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plt.ylabel(train_metric)
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plt.xlabel('Epoch')
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plt.legend(['train', 'validation'], loc='upper left')
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plt.savefig(f'{title}.png')
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# plt.show()
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show_train_history(train_history, 'Accuracy', 'accuracy', 'val_accuracy')
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show_train_history(train_history, 'Loss', 'loss', 'val_loss')
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lr_tracker.show_train_learningRate()
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# =============================================================================
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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。)
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# print('Test loss:', loss)
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# print('Test accuracy:', accuracy)
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model.save('model0504_3070.h5')
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print(model.summary())
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# # -*- coding: utf-8 -*-
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# """
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# Spyder Editor
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#
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# This is a temporary script file.
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#
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#
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# python 3.6
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# pip install tensorflow-gpu==2.6.0
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# pip install keras==2.6.0
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# pip install opencv-python==3.4.2.16
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# pip install matplotlib==3.3.4
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#
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# """
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# import numpy as np
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# import os
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# # import cv2
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# from sklearn.model_selection import train_test_split # pip install scikit-learn
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# from tensorflow import keras
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# from tensorflow.keras import layers
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# from tensorflow.keras import models
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# from tensorflow.keras.preprocessing.image import img_to_array
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# from tensorflow.keras.preprocessing.image import load_img
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# import tensorflow as tf
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# import os
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#
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#
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# class LearningRateTracker(keras.callbacks.Callback):
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# def __init__(self):
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# self._lr = list()
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#
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# def on_epoch_end(self, epoch, logs=None):
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# optimizer = self.model.optimizer
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# self._lr.append(tf.keras.backend.get_value(optimizer.lr))
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# print(f'Epoch {epoch + 1}: Learning rate is {tf.keras.backend.get_value(optimizer.lr)}.\n')
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#
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# def show_train_learningRate(self):
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# plt.plot(self._lr)
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# plt.title('learningRate')
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# plt.ylabel('learningRate')
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# plt.xlabel('Epoch')
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# plt.legend(['train', 'validation'], loc='upper left')
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# plt.savefig('LearningRate.png')
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# plt.show()
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#
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#
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# # os.environ['']
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# # =============================================================================
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# from keras.utils import np_utils
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#
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# # =============================================================================
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# config = tf.compat.v1.ConfigProto()
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# gpus = tf.config.experimental.list_physical_devices('GPU')
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# os.environ["CUDA_VISIBLE_DEVICES"] = '0'
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# session = tf.compat.v1.Session(config=config)
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#
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# epochs = 150 # 訓練的次數
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# batch_size = 16
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# img_rows = None # 驗證碼影像檔的高
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# img_cols = None # 驗證碼影像檔的寬
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# # digits_in_img = 6 #驗證碼影像檔中有幾位數
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# x_list = list() # 存所有驗證碼數字影像檔的array
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# y_list = list() # 存所有的驗證碼數字影像檔array代表的正確數字
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# x_train = list() # 存訓練用驗證碼數字影像檔的array
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# y_train = list() # 存訓練用驗證碼數字影像檔array代表的正確數字
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# x_test = list() # 存測試用驗證碼數字影像檔的array
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# y_test = list() # 存測試用驗證碼數字影像檔array代表的正確數字
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#
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#
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# def split_digits_in_img(img_array, x_list, y_list=None): # 副函式：分割及儲存驗證碼
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# # for i in range(digits_in_img):
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# # step = img_cols // digits_in_img #step=圖片總寬度除六
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# # y_list.append(img_filename[:2]) #將圖片正確數字(檔名)存進 y_list
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# x_list.append(img_array) # 將圖片存進x_list
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#
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#
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# input_filepath = './trainningset/0920 Milwaukee_TrainCreat'
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# file_list = sorted(os.listdir(input_filepath)) # 获取文件名列表.
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# print(f'file_list = {file_list}')
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# for classnumber in file_list:
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# img_filenames = os.listdir(f'{input_filepath}/{classnumber}')
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# 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
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# for img_filename in img_filenames:
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# count += 1
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# if '.png' not in img_filename: # 只讀取PNG檔
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# continue
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# # 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
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# 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())
|