Tensorflow2.1基础知识---丰富神经网络八股的内容

本篇文章的主要目的是对上一篇文章已经搭建好的网络进一步丰富其内容，具体体现在6个方面：

	1. 加载自己制作的训练集
	2. 数据增强
	3. 断点续训
	4. 参数提取
	5. acc/loss可视化
	6. 给图识物

1. 加载自己制作的训练集

   代码：

   import tensorflow as tf
   from PIL import Image
   import numpy as np
   import os
   
   #加载训练集的路径以及要保存的路径
   train_path = './mnist_image_label/mnist_train_jpg_60000/'
   train_txt = './mnist_image_label/mnist_train_jpg_60000.txt'
   x_train_savepath = './mnist_image_label/mnist_x_train.npy'
   y_train_savepath = './mnist_image_label/mnist_y_train.npy'
   #加载测试集的路径以要保存的路径
   test_path = './mnist_image_label/mnist_test_jpg_10000/'
   test_txt = './mnist_image_label/mnist_test_jpg_10000.txt'
   x_test_savepath = './mnist_image_label/mnist_x_test.npy'
   y_test_savepath = './mnist_image_label/mnist_y_test.npy'
   
   
   #--------------------------------------------------------------------------
   #---------------------------------自制数据集-------------------------------
   #--------------------------------------------------------------------------
   def generateds(path,txt):
       f = open(txt,'r')  # 以只读形式打开文件
       contents = f.readlines()  # 读取文件中所有行
       f.close()  # 关闭txt文件
       x,y_ = [],[]  # 建立空列表
       for content in contents:  # 逐行取出
           value = content.split()  # 以空格分开，图片路径为value[0],标签为value[1],存入列表
           img_path = path + value[0]  # 拼出图片路径和文件名
           img = Image.open(img_path)  # 读入图片
           img = np.array(img.convert('L'))  # 图片变为8位宽灰度值的np.array格式
           img = img / 255  # 数据归一化（实现预处理）
           x.append(img)  #归一化后的数据，添加到列表x
           y_.append(value[1])  #标签添加到列表y
           print('loading:' + content)  #打印状态提示
       x = np.array(x)  #变为np.array格式
       y_ = np.array(y_)  #变为np.array格式
       y_ = y_.astype(np.int64)  #变为64位整型
       return x,y_  #返回输入特征x，返回标签y_
   
   if os.path.exists(x_train_savepath) and os.path.exists(y_train_savepath) and os.path.exists(x_test_savepath) and os.path.exists(y_test_savepath):
       print('----------------Load Datasets---------------------')
       x_train_save = np.load(x_train_savepath)
       y_train = np.load(y_train_savepath)
       x_test_save = np.load(x_test_savepath)
       y_test = np.load(y_test_savepath)
       x_train = np.reshape(x_train_save,(len(x_train_save),28,28))
       x_test = np.reshape(x_test_save,(len(x_test_save),28,28))
   else:
       print('----------------Generate Datasets---------------------')
       x_train,y_train = generateds(train_path,train_txt)
       x_test,y_test = generateds(test_path,test_txt)
       
       print('----------------Save Datasets---------------------')
       x_train_save = np.reshape(x_train,(len(x_train), -1))
       x_test_save = np.reshape(x_test,(len(x_test), -1))
       np.save(x_train_savepath,x_train_save)
       np.save(y_train_savepath,y_train)
       np.save(x_test_savepath,x_test_save)
       np.save(y_test_savepath,y_test)
       
   
       
   
   model = tf.keras.models.Sequential([
       tf.keras.layers.Flatten(),
       tf.keras.layers.Dense(128,activation = 'relu'),
       tf.keras.layers.Dense(10,activation = 'softmax')
   ])
   
   model.compile(optimizer = 'adam',
                loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits = False),
                metrics = ['sparse_categorical_accuracy'])
   
   model.fit(x_train,y_train,batch_size = 32,epochs = 5,validation_data = (x_test,y_test),validation_freq=1)
   model.summary()
   		
   

2. 数据增强
   函数介绍：

    Image_gen_train = tf.keras.preprocessing.image.ImageDataGenerator(
    	Rescale = 所有数据将乘以该数值
    	Rotation_range = 随机旋转角度数范围
    	Width_shift_range = 随机宽度偏移量
    	Height_shift_range = 随机高度偏移量
    	水平翻转：horizontal_flip = 是否随机水平翻转
    	随机缩放：zoom_range = 随机缩放的范围[1-n,1+n]
    )
    Image_gen_train.fit(x_train)
   

   代码：

   import tensorflow as tf
   from tensorflow.keras.preprocessing.image import ImageDataGenerator
   
   mnist = tf.keras.datasets.mnist
   (x_train,y_train),(x_test,y_test) = mnist.load_data()
   x_train,x_test = x_train / 255.0,x_test / 255.0
   x_train = x_train.reshape(x_train.shape[0],28,28,1)  #给数据增减一个维度，从（60000，28，28）reshape为（60000，28，28，1）
   
   image_gen_train = ImageDataGenerator(
       rescale = 1. / 1.,
       rotation_range = 45,
       width_shift_range = .15,
       height_shift_range = .15,
       horizontal_flip = False,
       zoom_range = 0.5
   )
   image_gen_train.fit(x_train)
   
   model = tf.keras.models.Sequential([
       tf.keras.layers.Flatten(),
       tf.keras.layers.Dense(128,activation = 'relu'),
       tf.keras.layers.Dense(10,activation = 'softmax')
   ])
   
   model.compile(optimizer = 'adam',
                loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits = False),
                metrics = ['sparse_categorical_accuracy'])
   
   model.fit(image_gen_train.flow(x_train,y_train,batch_size = 32),epochs = 5,validation_data = (x_test,y_test),validation_freq=1)
   model.summary()
   

3. 断点续训
   a. 读取模型：

    函数：load_weights(路径文件名)
    例子：
    	checkpoint_save_path = "./checkpoint/mnist.ckpt"
    	if os.path.exists(checkpoint_save_path + '.index'):
    		print("--------------load the model---------------")
    		model.load_weights(checkpoint_save_path)
   

   b. 保存模型：

    函数：
    	tf.keras.callbacks.ModelCheckpoint(
    		filepath = 路径文件名,
    		save_weights_only = True/False,
    		save_best_only = True/False
    	)
    	history = mlodel.fit(callbacks = [cp_callback])
    例子：
    	cp_callback = tf.keras.callbacks.ModelCheckpoint(
    		filepath = checkpoint_save_path,
    		save_weights_only = True,
    		save_best_only = True)
    	history = model.fit(x_train,y_train,batch_size=32,epochs=5,validation_data=(x_test,y_test),validation_freq=1,callbacks = [cp_callback])
   

   代码：

   import tensorflow as tf
   import os
   
   
   mnist = tf.keras.datasets.mnist
   (x_train,y_train),(x_test,y_test) = mnist.load_data()
   x_train,x_test = x_train / 255.0,x_test / 255.0
   
   model = tf.keras.models.Sequential([
       tf.keras.layers.Flatten(),
       tf.keras.layers.Dense(128,activation = 'relu'),
       tf.keras.layers.Dense(10,activation = 'softmax')
   ])
   
   model.compile(optimizer = 'adam',
                loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits = False),
                metrics = ['sparse_categorical_accuracy'])
   
   checkpoint_save_path = "./checkpoint/mnist.ckpt"
   if os.path.exists(checkpoint_save_path + '.index'):
       print("--------------load the model---------------")
       model.load_weights(checkpoint_save_path)
   
   cp_callback = tf.keras.callbacks.ModelCheckpoint(
       filepath = checkpoint_save_path,
       save_weights_only = True,
       save_best_only = True)
   history = model.fit(x_train,y_train,batch_size=32,epochs=5,validation_data=(x_test,y_test),validation_freq=1,callbacks = [cp_callback])
   model.summary()
   

4. 参数提取
   代码：

   import tensorflow as tf
   import os
   import numpy as np
   np.set_printoptions(threshold = np.inf)
   
   mnist = tf.keras.datasets.mnist
   (x_train,y_train),(x_test,y_test) = mnist.load_data()
   x_train,x_test = x_train / 255.0,x_test / 255.0
   
   model = tf.keras.models.Sequential([
       tf.keras.layers.Flatten(),
       tf.keras.layers.Dense(128,activation = 'relu'),
       tf.keras.layers.Dense(10,activation = 'softmax')
   ])
   
   model.compile(optimizer = 'adam',
                loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits = False),
                metrics = ['sparse_categorical_accuracy'])
   
   checkpoint_save_path = "./checkpoint/mnist.ckpt"
   if os.path.exists(checkpoint_save_path + '.index'):
       print("--------------load the model---------------")
       model.load_weights(checkpoint_save_path)
   
   cp_callback = tf.keras.callbacks.ModelCheckpoint(
       filepath = checkpoint_save_path,
       save_weights_only = True,
       save_best_only = True)
   history = model.fit(x_train,y_train,batch_size=32,epochs=5,validation_data=(x_test,y_test),validation_freq=1,callbacks = [cp_callback])
   model.summary()
   
   #打印参数
   print(model.trainable_variables)
   #将参数保存到文件weights.txt中
   file = open('./weights.txt','w')
   for v in model.trainable_variables:
       file.write(str(v.name) + '\n')
       file.write(str(v.shape) + '\n')
       file.write(str(v.numpy()) + '\n')
   file.close()
   

5. acc/loss可视化
   history包含得出的结果：

   1. 训练集loss：loss
   2. 测试集loss：val_loss
   3. 训练集准确率：sparse_categorical_accuracy
   4. 测试集准确率：val_sparse_cateforical_accuracy

   代码：

   import tensorflow as tf
   import os
   import numpy as np
   from matplotlib import pyplot as plt
   np.set_printoptions(threshold = np.inf)
   
   mnist = tf.keras.datasets.mnist
   (x_train,y_train),(x_test,y_test) = mnist.load_data()
   x_train,x_test = x_train / 255.0,x_test / 255.0
   
   model = tf.keras.models.Sequential([
       tf.keras.layers.Flatten(),
       tf.keras.layers.Dense(128,activation = 'relu'),
       tf.keras.layers.Dense(10,activation = 'softmax')
   ])
   
   model.compile(optimizer = 'adam',
                loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits = False),
                metrics = ['sparse_categorical_accuracy'])
   
   checkpoint_save_path = "./checkpoint/mnist.ckpt"
   if os.path.exists(checkpoint_save_path + '.index'):
       print("--------------load the model---------------")
       model.load_weights(checkpoint_save_path)
   
   cp_callback = tf.keras.callbacks.ModelCheckpoint(
       filepath = checkpoint_save_path,
       save_weights_only = True,
       save_best_only = True)
   history = model.fit(x_train,y_train,batch_size=32,epochs=5,validation_data=(x_test,y_test),validation_freq=1,callbacks = [cp_callback])
   model.summary()
   
   print(model.trainable_variables)
   file = open('./weights.txt','w')
   for v in model.trainable_variables:
       file.write(str(v.name) + '\n')
       file.write(str(v.shape) + '\n')
       file.write(str(v.numpy()) + '\n')
   file.close()
   
   acc = history.history['sparse_categorical_accuracy']
   val_acc = history.history['val_sparse_categorical_accuracy']
   loss = history.history['loss']
   val_loss = history.history['val_loss']
   
   plt.subplot(1,2,1)
   plt.plot(acc,label="Training Accuracy")
   plt.plot(val_acc,label="Validation Accuracy")
   plt.title("Training abd Validation Accuracy")
   plt.legend()
   
   plt.subplot(1,2,2)
   plt.plot(loss,label="Training Loss")
   plt.plot(val_loss,label="Validation Loss")
   plt.title("Training abd Validation Loss")
   plt.legend()
   plt.show()
   

6. 给图识物（也就是前向传播的应用）

   1. 调用函数：predict(输入特征，batch_size=整数) 返回前向传播的计算结果

   2. 步骤：

       a. 浮现模型
       	model = tf.keras.models.Sequential([
       	    tf.keras.layers.Flatten(),
       	    tf.keras.layers.Dense(128, activation='relu'),
       	    tf.keras.layers.Dense(10, activation='softmax')])
       b. 加载参数
       	model.load_weights(model_save_path)
       c. 预测结果
       	result = model.predict(x_predict)
      

   代码：

   from PIL import Image
   import numpy as np
   import tensorflow as tf
   
   model_save_path = './checkpoint/mnist.ckpt'
   
   model = tf.keras.models.Sequential([
       tf.keras.layers.Flatten(),
       tf.keras.layers.Dense(128, activation='relu'),
       tf.keras.layers.Dense(10, activation='softmax')])
   
   model.load_weights(model_save_path)
   
   preNum = int(input("input the number of test pictures:"))
   
   for i in range(preNum):
       image_path = input("the path of test picture:")
       img = Image.open(image_path)
       img = img.resize((28, 28), Image.ANTIALIAS)
       img_arr = np.array(img.convert('L'))
   
       for i in range(28):
           for j in range(28):
               if img_arr[i][j] < 200:
                   img_arr[i][j] = 255
               else:
                   img_arr[i][j] = 0
   
       img_arr = img_arr / 255.0
       x_predict = img_arr[tf.newaxis, ...]
       result = model.predict(x_predict)
   
       pred = tf.argmax(result, axis=1)
   
       print('\n')
       tf.print(pred)
   

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