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mnist_cnn.py
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# -*- coding: utf-8 -*-
# @Time : 2021/6/29 7:08 下午
# @Author : Zijie Huang
# @FileName: mnist_cnn.py
# @Email : [email protected]
# @Software: PyCharm
import tensorflow as tf
tf.compat.v1.enable_eager_execution
import struct
import numpy as np
import tempfile
batch_size = 256
num_classes = 10
epochs=30
# input image dimensions
img_rows, img_cols = 28, 28
# the data, shuffled and split between train and test sets
#(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()
# 读MNIST数据集的图片数据
def mnist_load_img(img_path):
with open(img_path, "rb") as fp:
# >是以大端模式读取，i是整型模式，读取前四位的标志位，
# unpack()函数：是将4个字节联合后再解析成一个数，(读取后指针自动后移)
msb = struct.unpack('>i', fp.read(4))[0]
# 标志位为2051，后存图像数据；标志位为2049，后存图像标签
if msb == 2051:
# 读取样本个数60000，存入cnt
cnt = struct.unpack('>i', fp.read(4))[0]
# rows：行数28；cols：列数28
rows = struct.unpack('>i', fp.read(4))[0]
cols = struct.unpack('>i', fp.read(4))[0]
imgs = np.empty((cnt, rows, cols), dtype="int")
for i in range(0, cnt):
for j in range(0, rows):
for k in range(0, cols):
# 16进制转10进制
pxl = int(hex(fp.read(1)[0]), 16)
imgs[i][j][k] = pxl
return imgs
else:
return np.empty(1)
# 读MNIST数据集的图片标签
def mnist_load_label(label_path):
with open(label_path, "rb") as fp:
msb = struct.unpack('>i', fp.read(4))[0];
if msb == 2049:
cnt = struct.unpack('>i', fp.read(4))[0];
labels = np.empty(cnt, dtype="int");
for i in range(0, cnt):
label = int(hex(fp.read(1)[0]), 16);
labels[i] = label;
return labels;
else:
return np.empty(1);
x_train = mnist_load_img("./data/wm_64/train-images-idx3-ubyte")
y_train = mnist_load_label("./data/wm_64/train-labels-idx1-ubyte")
x_test = mnist_load_img("./data/wm_64/t10k-images-idx3-ubyte")
y_test = mnist_load_label("./data/wm_64/t10k-labels-idx1-ubyte")
'''x_train = mnist_load_img("./CGAN-CHAOS-MNIST-DATA/MNISTDATA16/train27/train-images-idx3-ubyte")
y_train = mnist_load_label("./CGAN-CHAOS-MNIST-DATA/MNISTDATA16/train27/train-labels-idx1-ubyte")
x_test = mnist_load_img("./CGAN-CHAOS-MNIST-DATA/MNISTDATA16/train27/t10k-images-idx3-ubyte")
y_test = mnist_load_label("./CGAN-CHAOS-MNIST-DATA/MNISTDATA16/train27/t10k-labels-idx1-ubyte")'''
if tf.keras.backend.image_data_format() == 'channels_first':
x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)
x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)
input_shape = (1, img_rows, img_cols)
else:
x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
input_shape = (img_rows, img_cols, 1)
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
print('x_train shape:', x_train.shape)
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')
# convert class vectors to binary class matrices
y_train = tf.keras.utils.to_categorical(y_train, num_classes)
y_test = tf.keras.utils.to_categorical(y_test, num_classes)
l = tf.keras.layers
model = tf.keras.Sequential([
l.Conv2D(
32, 5, padding='same', activation='relu', input_shape=input_shape),
l.MaxPooling2D((2, 2), (2, 2), padding='same'),
l.BatchNormalization(),
l.Conv2D(64, 5, padding='same', activation='relu'),
l.MaxPooling2D((2, 2), (2, 2), padding='same'),
l.Flatten(),
l.Dense(1024, activation='relu'),
l.Dropout(0.4),
l.Dense(num_classes, activation='softmax')
])
model.summary()
logdir = './log'
print('Writing training logs to ' + logdir)
callbacks = [tf.keras.callbacks.TensorBoard(log_dir=logdir, profile_batch=0)]
model.compile(
#loss=tf.keras.losses.categorical_crossentropy,
loss=tf.keras.losses.mean_squared_error,
optimizer='adam',
metrics=['accuracy'])
model.fit(x_train, y_train,
batch_size=batch_size,
epochs=epochs,
verbose=1,
callbacks=callbacks,
validation_data=(x_test, y_test))
score = model.evaluate(x_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])
# 保存模型
model.save('./model/mnist_cnn_64.h5')
'''
# Backend agnostic way to save/restore models
_, keras_file = tempfile.mkstemp('.h5')
print('Saving model to: ', keras_file)
tf.keras.models.save_model(model, keras_file, include_optimizer=False)
'''