tf.keras实现卷积神经网络

Keras 是一个用 Python 编写的高级神经网络 API，它能够以 TensorFlow, CNTK, 或者 Theano 作为后端运行。 Keras可以很明确的定义了层的概念，反过来层与层之间的参数反倒是用户不需要关心的对象，所以构建神经网络的方法对于普通开发者来说，相对tensorflow，Keras更易上手。 并且Keras也是tensorflow官方在tensorflow2.0开始极力推荐使用的。卷积神经网络（Convolutional Neural Networks, CNN）是一类包含卷积计算且具有深度结构的前馈神经网络（Feedforward Neural Networks），是深度学习（deep learning）的代表算法之一，对于图片（height，weight，channel）的输入数据，如果用DNN网络提取图片特征的话，那模型需要学习的参数每一层就有HWC这么多，这是一个呈几何倍数增长的数字，那么模型学习难度将会特别大，极易发生过拟合。考虑CNN网络，通过卷积核在图片上滑动进行卷积操作，参数量将会大大减少，并且卷积核可以提取图片特征向后传，最后通过全连接层对图片特征进行输出。首先导入一些需要使用的包，然后导入fashion_mnist数据集并分割好训练和测试集 import tensorflow as tf import numpy as np import matplotlib.pyplot as plt %matplotlib inline (train_image,train_label),(test_image,test_label) = tf.keras.datasets.fashion_mnist.load_data() print(train_image.shape) print(train_label) >> (60000, 28, 28) [9 0 0 ... 3 0 5] 由于tf.keras的卷积神经网络的训练需要是一个四维（num,hight,weight,channel）的数据，所以下面对输入图片数据拓宽一个通道 # 把输入数据拓宽一个维度，转换成（num,hight,weight,channel） train_images = np.expand_dims(train_image, -1) test_images = np.expand_dims(test_image, -1) train_images.shape >> (60000, 28, 28, 1) 建立顺序模型 # 新建顺序模型 model = tf.keras.Sequential() model.add(tf.keras.layers.Conv2D(32, (3,3), input_shape = train_images.shape[1: ], activation = 'relu')) # 输入维度为3，取图片的后三维 model.add(tf.keras.layers.MaxPool2D(pool_size = (2,2))) #下采样，降低参数数量，同时增强下一层卷积的视野 model.add(tf.keras.layers.Conv2D(64, kernel_size = (3,3), activation = 'relu')) model.add(tf.keras.layers.GlobalAveragePooling2D()) #把卷积之后的图片转换为2维，连到全连接层 model.add(tf.keras.layers.Dense(10, activation = 'softmax')) model.summary() >> Model: "sequential_1" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= conv2d_2 (Conv2D) (None, 26, 26, 32) 320 _________________________________________________________________ max_pooling2d_1 (MaxPooling2 (None, 13, 13, 32) 0 _________________________________________________________________ conv2d_3 (Conv2D) (None, 11, 11, 64) 18496 _________________________________________________________________ global_average_pooling2d_1 ( (None, 64) 0 _________________________________________________________________ dense_1 (Dense) (None, 10) 650 ================================================================= Total params: 19,466 Trainable params: 19,466 Non-trainable params: 0 _________________________________________________________________ 编译训练模型 # 模型编译 model.compile(optimizer='adam', loss = 'sparse_categorical_crossentropy', metrics = ['acc'] ) history = model.fit(train_images, train_label, epochs=20, validation_data=(test_images,test_label)) >> Train on 60000 samples, validate on 10000 samples Epoch 1/20 60000/60000 [==============================] - 32s 527us/sample - loss: 0.7983 - acc: 0.7568 - val_loss: 0.5376 - val_acc: 0.8111 Epoch 2/20 60000/60000 [==============================] - 32s 529us/sample - loss: 0.4884 - acc: 0.8296 - val_loss: 0.4993 - val_acc: 0.8262 ... Epoch 14/20 60000/60000 [==============================] - 26s 441us/sample - loss: 0.2627 - acc: 0.9064 - val_loss: 0.3258 - val_acc: 0.8891 Epoch 15/20 60000/60000 [==============================] - 28s 460us/sample - loss: 0.2544 - acc: 0.9100 - val_loss: 0.3256 - val_acc: 0.8876 Epoch 16/20 60000/60000 [==============================] - 31s 524us/sample - loss: 0.2499 - acc: 0.9105 - val_loss: 0.3323 - val_acc: 0.8868 Epoch 17/20 60000/60000 [==============================] - 35s 576us/sample - loss: 0.2455 - acc: 0.9121 - val_loss: 0.3455 - val_acc: 0.8830 Epoch 18/20 60000/60000 [==============================] - 29s 491us/sample - loss: 0.2382 - acc: 0.9144 - val_loss: 0.3173 - val_acc: 0.8884 Epoch 19/20 60000/60000 [==============================] - 30s 500us/sample - loss: 0.2348 - acc: 0.9151 - val_loss: 0.3155 - val_acc: 0.8880 Epoch 20/20 60000/60000 [==============================] - 32s 531us/sample - loss: 0.2310 - acc: 0.9167 - val_loss: 0.3366 - val_acc: 0.8834 画图更直观的查看模型的训练情况 plt.figure(figsize=(20,8),dpi = 200) plt.plot(history.epoch, history.history.get('acc'), label = 'acc') plt.plot(history.epoch, history.history.get('val_acc'), label = 'val_acc') plt.legend() >> <matplotlib.legend.Legend at 0x18f7148ecc8>

通过绘制训练epochs与训练数据集的准确率和测试数据集的准确率折线图的观察到，模型在训练数据集上的准确率比验证数据集上的准确率高，而且模型的准确率在最后还一直呈现上升趋势，说明模型过拟合了并且模型的没有完全达到状态，下面对其进行优化。

优化模型，增加卷积层，提升模型拟合能力；添加Dropout层，防止过拟合。

首先新建顺序模型，增加了一个隐藏层，并且每个隐藏层的输出通道数也增加了，因为通道数是用来传递数据集特征的，通道数过小有可能不能承接图片的全部特征导致部分特征丢失，模型效果变差。然后在每个卷积层之后添加一个Dropout层防止过拟合，最后利用softmax激活输出10中类别。 # 新建顺序模型 model = tf.keras.Sequential() model.add(tf.keras.layers.Conv2D(64, (3,3), input_shape = train_images.shape[1: ], activation = 'relu',padding = "same")) # 输入维度为3，取图片的后三维 model.add(tf.keras.layers.Conv2D(64, kernel_size = (3,3), activation = 'relu', padding = 'same')) model.add(tf.keras.layers.Dropout(0.5)) model.add(tf.keras.layers.MaxPool2D(pool_size = (2,2))) # 下采样，降低参数数量，同时增强下一层卷积的视野 model.add(tf.keras.layers.Conv2D(128, kernel_size = (3,3), activation = 'relu',padding = 'same')) model.add(tf.keras.layers.Dropout(0.5)) model.add(tf.keras.layers.MaxPool2D(pool_size = (2,2))) model.add(tf.keras.layers.Conv2D(256, kernel_size = (3,3), activation = 'relu')) model.add(tf.keras.layers.Dropout(0.5)) model.add(tf.keras.layers.GlobalAveragePooling2D()) # 把卷积之后的图片转换为2维，连到全连接层 model.add(tf.keras.layers.Dense(10, activation = 'softmax')) model.summary() >> Model: "sequential_1" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= conv2d_4 (Conv2D) (None, 28, 28, 64) 640 _________________________________________________________________ conv2d_5 (Conv2D) (None, 28, 28, 64) 36928 _________________________________________________________________ dropout (Dropout) (None, 28, 28, 64) 0 _________________________________________________________________ max_pooling2d_2 (MaxPooling2 (None, 14, 14, 64) 0 _________________________________________________________________ conv2d_6 (Conv2D) (None, 14, 14, 128) 73856 _________________________________________________________________ dropout_1 (Dropout) (None, 14, 14, 128) 0 _________________________________________________________________ max_pooling2d_3 (MaxPooling2 (None, 7, 7, 128) 0 _________________________________________________________________ conv2d_7 (Conv2D) (None, 5, 5, 256) 295168 _________________________________________________________________ dropout_2 (Dropout) (None, 5, 5, 256) 0 _________________________________________________________________ global_average_pooling2d_1 ( (None, 256) 0 _________________________________________________________________ dense_1 (Dense) (None, 10) 2570 ================================================================= Total params: 409,162 Trainable params: 409,162 Non-trainable params: 0 _________________________________________________________________ 模型编译 model.compile( optimizer='adam', loss = 'sparse_categorical_crossentropy', metrics = ['acc'] ) history = model.fit(train_images, train_label, epochs=20, validation_data=(test_images,test_label)) >> Epoch 1/20 1875/1875 [==============================] - 8s 4ms/step - loss: 0.7331 - acc: 0.7903 - val_loss: 0.5045 - val_acc: 0.8555 Epoch 2/20 1875/1875 [==============================] - 8s 4ms/step - loss: 0.3989 - acc: 0.8558 - val_loss: 0.4424 - val_acc: 0.8773 Epoch 3/20 1875/1875 [==============================] - 8s 4ms/step - loss: 0.3585 - acc: 0.8683 - val_loss: 0.4049 - val_acc: 0.8851 Epoch 4/20 1875/1875 [==============================] - 8s 4ms/step - loss: 0.3288 - acc: 0.8810 - val_loss: 0.3946 - val_acc: 0.8867 Epoch 5/20 1875/1875 [==============================] - 8s 4ms/step - loss: 0.3084 - acc: 0.8876 - val_loss: 0.3423 - val_acc: 0.9027 Epoch 6/20 1875/1875 [==============================] - 8s 4ms/step - loss: 0.2901 - acc: 0.8941 - val_loss: 0.3753 - val_acc: 0.8991 Epoch 7/20 1875/1875 [==============================] - 8s 4ms/step - loss: 0.2826 - acc: 0.8963 - val_loss: 0.3452 - val_acc: 0.9002 Epoch 8/20 1875/1875 [==============================] - 8s 4ms/step - loss: 0.2719 - acc: 0.9010 - val_loss: 0.3152 - val_acc: 0.9117 Epoch 9/20 1875/1875 [==============================] - 8s 4ms/step - loss: 0.2663 - acc: 0.9027 - val_loss: 0.3082 - val_acc: 0.9103 Epoch 10/20 1875/1875 [==============================] - 8s 4ms/step - loss: 0.2605 - acc: 0.9038 - val_loss: 0.2891 - val_acc: 0.9025 Epoch 11/20 1875/1875 [==============================] - 8s 4ms/step - loss: 0.2544 - acc: 0.9065 - val_loss: 0.3096 - val_acc: 0.9154 Epoch 12/20 1875/1875 [==============================] - 8s 4ms/step - loss: 0.2512 - acc: 0.9088 - val_loss: 0.2958 - val_acc: 0.9105 Epoch 13/20 1875/1875 [==============================] - 8s 4ms/step - loss: 0.2469 - acc: 0.9092 - val_loss: 0.2882 - val_acc: 0.9178 Epoch 14/20 1875/1875 [==============================] - 8s 4ms/step - loss: 0.2436 - acc: 0.9108 - val_loss: 0.2783 - val_acc: 0.9124 Epoch 15/20 1875/1875 [==============================] - 8s 4ms/step - loss: 0.2400 - acc: 0.9123 - val_loss: 0.2788 - val_acc: 0.9185 Epoch 16/20 1875/1875 [==============================] - 8s 4ms/step - loss: 0.2400 - acc: 0.9125 - val_loss: 0.2649 - val_acc: 0.9191 Epoch 17/20 1875/1875 [==============================] - 8s 4ms/step - loss: 0.2376 - acc: 0.9136 - val_loss: 0.2707 - val_acc: 0.9193 Epoch 18/20 1875/1875 [==============================] - 8s 4ms/step - loss: 0.2368 - acc: 0.9139 - val_loss: 0.2546 - val_acc: 0.9191 Epoch 19/20 1875/1875 [==============================] - 8s 4ms/step - loss: 0.2327 - acc: 0.9155 - val_loss: 0.3030 - val_acc: 0.9174 Epoch 20/20 1875/1875 [==============================] - 8s 4ms/step - loss: 0.2314 - acc: 0.9154 - val_loss: 0.2615 - val_acc: 0.9239 下面绘制训练轮数epochs与准确率之间的折线图 plt.figure(figsize=(20,8),dpi = 200) plt.plot(history.epoch, history.history.get('acc'), label = 'acc') plt.plot(history.epoch, history.history.get('val_acc'), label = 'val_acc') plt.legend() >> <matplotlib.legend.Legend at 0x7f85d0736990>

可以看到测试数据集的准确率一直在训练数据集之上，说明模型没有过拟合，但是训练数据集依然有上升的趋势，说明模型还是训练不足，可以考虑继续优化。