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import os |
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import tensorflow as tf |
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import matplotlib.pyplot as plt |
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import numpy as np |
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mnist = tf.keras.datasets.mnist |
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(x_train, y_train),(x_test, y_test) = mnist.load_data(path="/mnist.npz") #加载mnist数据集 |
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#验证mnist数据集大小。x为数据，y为标签。mnist每张图的像素为28*28 |
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print(x_train.shape) |
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print(y_train.shape) |
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print(x_test.shape) |
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print(y_test.shape) |
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#打印训练集中前9张，看看是什么数字 |
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for i in range(9): |
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plt.subplot(3,3,1+i) |
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plt.imshow(x_train[i], cmap='gray') |
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plt.show() |
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plt.savefig('./mnist/output/1.jpg') |
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#打印相应的标签 |
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print(y_train[:9]) |
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#基操：将像素标准化一下 |
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x_train, x_test = x_train / 255.0, x_test / 255.0 |
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#搭建一个两层神经网络 |
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model = tf.keras.models.Sequential([ |
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tf.keras.layers.Flatten(input_shape=(28, 28)), #拉伸图像成一维向量 |
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tf.keras.layers.Dense(128, activation='relu'), #第一层全连接+ReLU激活 |
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tf.keras.layers.Dropout(0.2), #dropout层 |
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tf.keras.layers.Dense(10, activation='softmax') #第二层全连接+softmax激活，输出预测标签 |
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]) |
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#设置训练超参，优化器为sgd，损失函数为交叉熵，训练衡量指标为accuracy |
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model.compile(optimizer='sgd', loss='sparse_categorical_crossentropy', metrics=['accuracy']) |
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#开始训练，训练5个epoch，一个epoch代表所有图像计算一遍。每一个epoch能观察到训练精度的提升 |
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model.fit(x_train, y_train, epochs=5) |
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#计算训练了5个epoch的模型在测试集上的表现 |
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model.evaluate(x_test, y_test) |
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#直观看一下模型预测结果，打印测试集中的前9张图像 |
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for i in range(9): |
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plt.subplot(3,3,1+i) |
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plt.imshow(x_test[i], cmap='gray') |
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plt.show() |
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plt.savefig('./mnist/output/2.jpg') |
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#打印模型识别的数字，是否正确？ |
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# np.argmax(model(x_test[:9]).numpy(), axis=1) |
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#保存训练好的模型 |
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model.save("./mnist/output/model_epoch_5") |
