update

code/.idea/vcs.xml

@@ -0,0 +1,6 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="VcsDirectoryMappings">
+    <mapping directory="$PROJECT_DIR$/.." vcs="Git" />
+  </component>
+</project>

code/checkpoint/checkpoint

@@ -1,2 +1,2 @@
-model_checkpoint_path: "mnist.ckpt"
-all_model_checkpoint_paths: "mnist.ckpt"
+model_checkpoint_path: "mnist_cnn3.ckpt"
+all_model_checkpoint_paths: "mnist_cnn3.ckpt"

code/main.py

@@ -51,7 +51,6 @@ def screenshot(*args):
     im.save('1.jpg')
     print("在使用%d模型"%model)
     result = read_image.predeiction('1.jpg', model)
-
     text.set(str(result))
 
 # 此函数用于清空画布

code/mnist_cnn.py

@@ -1,52 +1,82 @@
 import tensorflow as tf
+import tensorflow.keras as keras
 import os
 from matplotlib import pyplot as plt
 from PIL import Image
 import numpy as np
 
-mnist = tf.keras.datasets.mnist
-
+mnist = keras.datasets.mnist
+# 载入mnist数据集
 (x_train, y_train), (x_test, y_test) = mnist.load_data()
-x_train, x_test = x_train/255.0, x_test/255.0
+
+# 归一化处理样本，把每个像素点的数据范围由[0-255]转为[0-1]
+x_train = x_train/255.0
+x_test = x_test/255.0
+
+# 增加一个维度
+x_train = tf.expand_dims(x_train, -1)
+x_test = tf.expand_dims(x_test, -1)
 
 def creat_model():
-    model = tf.keras.models.Sequential([
-        tf.keras.layers.Flatten(),
-        tf.keras.layers.Dense(128, activation='relu'),
-        tf.keras.layers.Dense(10, activation='softmax')
+
+    model = keras.Sequential([
+        # 卷积层定义
+        keras.layers.Conv2D(32, 3, padding="SAME"),
+        keras.layers.BatchNormalization(),
+        keras.layers.Activation("relu"),
+        keras.layers.MaxPool2D(pool_size=(2, 2), strides=2),
+        keras.layers.Dropout(0.2),
+        # 分类器定义
+        keras.layers.Flatten(),
+        keras.layers.Dense(128, activation="relu"),
+        keras.layers.Dense(10, activation="softmax")
     ])
 
-    model.compile(optimizer='adam',
-                  loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False),
+    model.compile(optimizer='adam',                                                  # 优化方法选用adam
+                  loss=keras.losses.SparseCategoricalCrossentropy(from_logits=False),
+                                                                                     # 指定每个批次训练误差的减小方法
                   metrics=["sparse_categorical_accuracy"])
+                                                                                     # 评价函数
     return model
-def model_fit(model,check_save_path):
 
+
+def eva_acc(str, model):
+    # 加载测试集判断准确率
+    model.load_weights(str)
+    final_loss, final_acc = model.evaluate(x_test, y_test, verbose=2)
+    print("Model accuracy: ", final_acc, ", model loss: ", final_loss)
+
+
+
+def model_fit(model, check_save_path):
     if os.path.exists(check_save_path+'.index'):
         print("load modals...")
         model.load_weights(check_save_path)
-
     cp_callback = tf.keras.callbacks.ModelCheckpoint(filepath=check_save_path,
                                                     save_weights_only=True,
                                                     save_best_only=True)
+    ES_callback = keras.callbacks.EarlyStopping(
+        monitor="val_sparse_categorical_accuracy",            # 数据的监视入口
+        min_delta=0.001,                   # 增大或减小的阈值，只有大于这个部分才算作improvement。
+        patience=1,                        # 能够容忍多少个epo
+        # ch内都没有improvement。
+        mode='max'                         # ’auto’, ‘min’, ‘,max’三个可能。如果知道是要上升还是下降，建议设置一下。
+    )
 
-    history = model.fit(x_train, y_train, batch_size=32, epochs=5, validation_split=0.15, validation_freq=1, callbacks=[cp_callback])
-    model.summary()
-
-    final_loss, final_acc = model.evaluate(x_test, y_test, verbose=2)
-    print("Model accuracy: ", final_acc, ", model loss: ", final_loss)
 
+    history = model.fit(x_train, y_train, batch_size=32, epochs=5, validation_split=0.15, validation_freq=1, callbacks=[cp_callback, ES_callback])
+    # 输出参数计算
+    model.summary()
     acc = history.history['sparse_categorical_accuracy']
     val_acc = history.history['val_sparse_categorical_accuracy']
     loss = history.history['loss']
     val_loss = history.history['val_loss']
-
+    # 图形化
     plt.subplot(1, 2, 1)
     plt.plot(acc, label='Training Accuracy')
     plt.plot(val_acc, label='Validation Accuracy')
     plt.title('Training and Validation Accuracy')
     plt.legend()
-
     plt.subplot(1, 2, 2)
     plt.plot(loss, label='Training Loss')
     plt.plot(val_loss, label='Validation Loss')
@@ -54,13 +84,9 @@ def model_fit(model,check_save_path):
     plt.legend()
     plt.show()
 
-def eva_acc(str, model):
-    model.load_weights(str)
-    final_loss, final_acc = model.evaluate(x_test, y_test, verbose=2)
-    print("Model accuracy: ", final_acc, ", model loss: ", final_loss)
 
 if __name__=="__main__":
-    check_save_path = "./checkpoint/mnist.ckpt"
+    check_save_path = "./checkpoint/mnist_cnn1.ckpt"
     model = creat_model()
     model_fit(model, check_save_path)
     eva_acc(check_save_path, model)

code/mnist_cnn3.py

@@ -5,14 +5,16 @@ import tensorflow as tf
 import matplotlib.pyplot as plt
 mnist = keras.datasets.mnist
 (x_train, y_train), (x_test, y_test) = mnist.load_data()
+# 数据归一化
 x_train = x_train/255.0
 x_test = x_test/255.0
 
+# 为了卷积层输入格式，增加一个维度
 x_train = tf.expand_dims(x_train, -1)
 x_test = tf.expand_dims(x_test, -1)
 
-print("train shape:", x_train.shape)
-print("test shape:", x_test.shape)
+# print("train shape:", x_train.shape)
+# print("test shape:", x_test.shape)
 
 # 使用此类进行图形增强
 datagen = keras.preprocessing.image.ImageDataGenerator(
@@ -24,6 +26,7 @@ datagen = keras.preprocessing.image.ImageDataGenerator(
     horizontal_flip=False                               # 布尔值,随机水平翻转。
 )
 
+
 train_datagen = datagen.flow(
     x_train,
     y_train,
@@ -38,20 +41,31 @@ validation_genetor = datagen.flow(
     subset="validation"
 )
 def creat_model():
+    """
+    此函数用于构建模型。
+    调用keras.Sequential()建立模型的框架。
+    使用compile()配置神经网络训练方法
+    :return:
+    """
     model = keras.Sequential([
         keras.layers.Reshape((28, 28, 1)),
+        # 卷积层定义
         keras.layers.Conv2D(filters=32, kernel_size=(5, 5), activation="relu", padding="same",
                                input_shape=(28, 28, 1)),
+        # 池化层定义
         keras.layers.MaxPool2D((2, 2)),
 
+        # 卷积层、池化层
         keras.layers.Conv2D(filters=64, kernel_size=(3, 3), activation="relu", padding="same"),
         keras.layers.Conv2D(filters=64, kernel_size=(3, 3), activation="relu", padding="same"),
         keras.layers.MaxPool2D((2, 2)),
 
+        # 卷积层、池化层
         keras.layers.Conv2D(filters=128, kernel_size=(3, 3), activation="relu", padding="same"),
         keras.layers.Conv2D(filters=128, kernel_size=(3, 3), activation="relu", padding="same"),
         keras.layers.MaxPool2D((2, 2)),
 
+        # 分类器：多个全连接层
         keras.layers.Flatten(),
         keras.layers.Dense(512, activation="sigmoid"),
         keras.layers.Dropout(0.25),
@@ -65,29 +79,53 @@ def creat_model():
         keras.layers.Dense(10, activation="sigmoid")
         ])
 
+    # 使用compile()配置神经网络训练方法
+    # model.compile(optimizer=优化器,
+    #               loss=损失函数,
+    #               metrics=["评测指标"])
     model.compile(optimizer='adam',
                   loss=keras.losses.SparseCategoricalCrossentropy(from_logits=False),
                   metrics=["sparse_categorical_accuracy"])
     return model
 
 def model_fit(model, check_save_path):
+
+# 判断是否有保存的模型，有就加载之
     if os.path.exists(check_save_path+'.index'):
         print("load modals...")
         model.load_weights(check_save_path)
 
+    # 使用callbacks.ModelCheckpoint()保存模型
+    # tf.keras.callbacks.ModelCheckpoint(filepath=保存模型的路径,
+    #                                                save_weights_only=若设置为True则仅保存模型权重,设置为False则保存整个模型
+    #                                                save_best_only=若设置为True将只保存在验证集上性能最好的模型
+    #                                                )
     cp_callback = tf.keras.callbacks.ModelCheckpoint(filepath=check_save_path,
                                                     save_weights_only=True,
                                                     save_best_only=True)
+    # 用于动态调整学习率，参数如下：
+    # monitor：监测的值，可以是accuracy，val_loss,val_accuracy
+    # factor：缩放学习率的值，学习率将以lr = lr*factor的形式被减少
+    # patience：当patience个epoch过去而模型性能不提升时，学习率减少的动作会被触发
+    # min_lr：学习率最小值，能缩小到的下限
     reduce_lr = tf.keras.callbacks.ReduceLROnPlateau(monitor='val_loss',
                                                      factor=0.1,
-                                                     patience=5,
+                                                     patience=2,
                                                      min_lr=0.000001,
                                                      verbose=1)
 
-
-    history = model.fit(train_datagen, epochs=1, validation_data=validation_genetor, callbacks=[reduce_lr,cp_callback],verbose=1)
+    # 早停机制
+    # monitor：要监测的数量
+    # min_delta：在被监测的数据中被认为是提升的最小变化，即绝对变化小于min_delta，将被视为没有提升。
+    # patience：没有进步的训练轮数，在这之后训练就会被停止。
+    earlystop_callback = keras.callbacks.EarlyStopping(
+      monitor='val_sparse_categorical_accuracy',
+      min_delta=0.0001,
+      patience=2)
+    history = model.fit(train_datagen, epochs=10, validation_data=validation_genetor, callbacks=[reduce_lr, cp_callback, earlystop_callback], verbose=1)
     model.summary()
 
+    # 用于可视化
     acc = history.history['sparse_categorical_accuracy']
     val_acc = history.history['val_sparse_categorical_accuracy']
     loss = history.history['loss']
@@ -106,7 +144,7 @@ def model_fit(model, check_save_path):
     plt.legend()
     plt.show()
 
-
+# 此函数通过测试集检测准确率
 def model_valtest(model, check_save_path):
     model.load_weights(check_save_path)
     final_loss, final_acc = model.evaluate(x_test,  y_test, verbose=2)
@@ -116,5 +154,5 @@ if __name__ == "__main__":
 
     check_save_path = "./checkpoint/mnist_cnn3.ckpt"
     model = creat_model()
-    # model_fit(model, check_save_path)
+    #model_fit(model, check_save_path)
     model_valtest(model, check_save_path)