【Python】keras卷积神经网络识别mnist
卷积神经网络的结构我随意设了一个。
结构大概是下面这个样子:
代码如下:
import numpy as np from keras.preprocessing import image from keras.models import Sequential from keras.layers import Dense, Dropout, Flatten, Activation from keras.layers import Conv2D, MaxPooling2D # 从文件夹图像与标签文件载入数据 def create_x(filenum, file_dir): train_x = [] for i in range(filenum): img = image.load_img(file_dir + str(i) + ".bmp", target_size=(28, 28)) img = img.convert('L') x = image.img_to_array(img) train_x.append(x) train_x = np.array(train_x) train_x = train_x.astype('float32') train_x /= 255 return train_x def create_y(classes, filename): train_y = [] file = open(filename, "r") for line in file.readlines(): tmp = [] for j in range(classes): if j == int(line): tmp.append(1) else: tmp.append(0) train_y.append(tmp) file.close() train_y = np.array(train_y).astype('float32') return train_y classes = 10 X_train = create_x(55000, './train/') X_test = create_x(10000, './test/') Y_train = create_y(classes, 'train.txt') Y_test = create_y(classes, 'test.txt') # 从网络下载的数据集直接解析数据 ''' from tensorflow.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets("MNIST_data/", one_hot=True) X_train, Y_train = mnist.train.images, mnist.train.labels X_test, Y_test = mnist.test.images, mnist.test.labels X_train = X_train.astype('float32') X_test = X_test.astype('float32') ''' model = Sequential() model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=(28, 28, 1))) model.add(Conv2D(64, (3, 3), activation='relu')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Dropout(0.25)) model.add(Conv2D(64, (3, 3), activation='relu')) model.add(Conv2D(64, (3, 3), activation='relu')) model.add(Dropout(0.25)) model.add(Flatten()) model.add(Dense(81, activation='relu')) model.add(Dropout(0.5)) model.add(Dense(10)) model.add(Activation('softmax')) model.summary() model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy']) history = model.fit(X_train, Y_train, batch_size=500, epochs=10, verbose=1, validation_data=(X_test, Y_test)) score = model.evaluate(X_test, Y_test, verbose=0) test_result = model.predict(X_test) result = np.argmax(test_result, axis=1) print(result) print('Test score:', score[0]) print('Test accuracy:', score[1])
最终在测试集上识别率在99%左右。
相关测试数据可以在这里下载到。