feedforward fashion-mnist

feedforward/main.py

+import os
+import struct
+import sys
+from easydict import EasyDict as edict
+
+import matplotlib.pyplot as plt
+import numpy as np
+
+import mindspore
+import mindspore.dataset as ds
+import mindspore.nn as nn
+from mindspore import context
+from mindspore.nn.metrics import Accuracy
+from mindspore.train import Model
+from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor, TimeMonitor
+from mindspore import Tensor
+
+context.set_context(mode=context.GRAPH_MODE, device_target='Ascend')
+
+
+def read_image(file_name):
+    '''
+    :param file_name: 文件路径
+    :return:  训练或者测试数据
+    如下是训练的图片的二进制格式
+    [offset] [type]          [value]          [description]
+    0000     32 bit integer  0x00000803(2051) magic number
+    0004     32 bit integer  60000            number of images
+    0008     32 bit integer  28               number of rows
+    0012     32 bit integer  28               number of columns
+    0016     unsigned byte   ??               pixel
+    0017     unsigned byte   ??               pixel
+    ........
+    xxxx     unsigned byte   ??               pixel
+    '''
+    file_handle = open(file_name, "rb")  # 以二进制打开文档
+    file_content = file_handle.read()  # 读取到缓冲区中
+    head = struct.unpack_from('>IIII', file_content, 0)  # 取前4个整数，返回一个元组
+    offset = struct.calcsize('>IIII')
+    imgNum = head[1]  # 图片数
+    width = head[2]  # 宽度
+    height = head[3]  # 高度
+    bits = imgNum * width * height  # data一共有60000*28*28个像素值
+    bitsString = '>' + str(bits) + 'B'  # fmt格式：'>47040000B'
+    imgs = struct.unpack_from(bitsString, file_content, offset)  # 取data数据，返回一个元组
+    imgs_array = np.array(imgs).reshape((imgNum, width * height))  # 最后将读取的数据reshape成 【图片数，图片像素】二维数组
+    return imgs_array
+
+
+def read_label(file_name):
+    '''
+    :param file_name:
+    :return:
+    标签的格式如下：
+    [offset] [type]          [value]          [description]
+    0000     32 bit integer  0x00000801(2049) magic number (MSB first)
+    0004     32 bit integer  60000            number of items
+    0008     unsigned byte   ??               label
+    0009     unsigned byte   ??               label
+    ........
+    xxxx     unsigned byte   ??               label
+    The labels values are 0 to 9.
+    '''
+    file_handle = open(file_name, "rb")  # 以二进制打开文档
+    file_content = file_handle.read()  # 读取到缓冲区中
+    head = struct.unpack_from('>II', file_content, 0)  # 取前2个整数，返回一个元组
+    offset = struct.calcsize('>II')
+    labelNum = head[1]  # label数
+    bitsString = '>' + str(labelNum) + 'B'  # fmt格式：'>47040000B'
+    label = struct.unpack_from(bitsString, file_content, offset)  # 取data数据，返回一个元组
+    return np.array(label)
+
+
+def get_data():
+    # 文件获取
+    train_image = os.path.join(cfg.data_dir_train, 'train-images-idx3-ubyte')
+    test_image = os.path.join(cfg.data_dir_test, "t10k-images-idx3-ubyte")
+    train_label = os.path.join(cfg.data_dir_train, "train-labels-idx1-ubyte")
+    test_label = os.path.join(cfg.data_dir_test, "t10k-labels-idx1-ubyte")
+    # 读取数据
+    train_x = read_image(train_image)
+    test_x = read_image(test_image)
+    train_y = read_label(train_label)
+    test_y = read_label(test_label)
+    return train_x, train_y, test_x, test_y
+
+
+# 定义前馈神经网络
+class Forward_fashion(nn.Cell):
+    def __init__(self, num_class=10):  # 一共分十类，图片通道数是1
+        super(Forward_fashion, self).__init__()
+        self.num_class = num_class
+        self.flatten = nn.Flatten()
+        self.fc1 = nn.Dense(cfg.channel * cfg.image_height * cfg.image_width, 128)
+        self.relu = nn.ReLU()
+        self.fc2 = nn.Dense(128, self.num_class)
+        self.softmax = nn.Softmax()
+
+    def construct(self, x):
+        x = self.flatten(x)
+        x = self.fc1(x)
+        x = self.relu(x)
+        x = self.fc2(x)
+        x = self.softmax(x)
+        return x
+
+
+import argparse
+parser = argparse.ArgumentParser()
+parser.add_argument('--data_url', required=True, default=None, help='Location of data.')
+parser.add_argument('--train_url', required=True, default=None, help='Location of training outputs.')
+args, unknown = parser.parse_known_args()
+
+import moxing as mox
+mox.file.copy_parallel(src_url=args.data_url, dst_url='Fashion-MNIST')
+
+cfg = edict({
+    'train_size': 60000,  # 训练集大小
+    'test_size': 10000,  # 测试集大小
+    'channel': 1,  # 图片通道数
+    'image_height': 28,  # 图片高度
+    'image_width': 28,  # 图片宽度
+    'batch_size': 60,
+    'num_classes': 10,  # 分类类别
+    'lr': 0.001,  # 学习率
+    'epoch_size': 20,  # 训练次数
+    'data_dir_train': os.path.join('Fashion-MNIST', 'train'),
+    'data_dir_test': os.path.join('Fashion-MNIST', 'test'),
+    'save_checkpoint_steps': 1,  # 多少步保存一次模型
+    'keep_checkpoint_max': 3,  # 最多保存多少个模型
+    'output_directory': './model_fashion',  # 保存模型路径
+    'output_prefix': "checkpoint_fashion_forward"  # 保存模型文件名字
+})
+
+train_x, train_y, test_x, test_y = get_data()
+train_x = train_x.reshape(-1, 1, 28, 28)
+test_x = test_x.reshape(-1, 1, 28, 28)
+train_x = train_x / 255.0
+test_x = test_x / 255.0
+train_x = train_x.astype('Float32')
+test_x = test_x.astype('Float32')
+train_y = train_y.astype('int32')
+test_y = test_y.astype('int32')
+print('训练数据集样本数：', train_x.shape[0])
+print('测试数据集样本数：', test_y.shape[0])
+print('通道数/图像长/宽：', train_x.shape[1:])
+print('一张图像的标签样式：', train_y[0])  # 一共10类，用0-9的数字表达类别。
+
+# 转换数据类型为Dataset
+XY_train = list(zip(train_x, train_y))
+ds_train = ds.GeneratorDataset(XY_train, ['x', 'y'])
+ds_train.set_dataset_size(cfg.train_size)
+ds_train = ds_train.shuffle(buffer_size=cfg.train_size).batch(cfg.batch_size, drop_remainder=True).repeat(
+    cfg.epoch_size)
+XY_test = list(zip(test_x, test_y))
+ds_test = ds.GeneratorDataset(XY_test, ['x', 'y'])
+ds_test.set_dataset_size(cfg.test_size)
+ds_test = ds_test.shuffle(buffer_size=cfg.test_size).batch(cfg.batch_size, drop_remainder=True).repeat(cfg.epoch_size)
+
+# 构建网络
+network = Forward_fashion(cfg.num_classes)
+# 定义模型的损失函数，优化器
+net_loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction="mean")
+net_opt = nn.Adam(network.trainable_params(), cfg.lr)
+# 训练模型
+model = Model(network, loss_fn=net_loss, optimizer=net_opt, metrics={"acc"})
+loss_cb = LossMonitor(per_print_times=int(cfg.train_size / cfg.batch_size))
+config_ck = CheckpointConfig(save_checkpoint_steps=cfg.save_checkpoint_steps,
+                             keep_checkpoint_max=cfg.keep_checkpoint_max)
+ckpoint_cb = ModelCheckpoint(prefix=cfg.output_prefix, directory=cfg.output_directory, config=config_ck)
+print("============== Starting Training ==============")
+model.train(cfg.epoch_size, ds_train, callbacks=[ckpoint_cb, loss_cb], dataset_sink_mode=True)
+
+# 使用测试集评估模型，打印总体准确率
+metric = model.eval(ds_test)
+print(metric)
+
+# 预测
+test_ = ds_test.create_dict_iterator().get_next()
+test = Tensor(test_['x'], mindspore.float32)
+predictions = model.predict(test)
+predictions = predictions.asnumpy()
+for i in range(10):
+    p_np = predictions[i, :]
+    p_list = p_np.tolist()
+    print('第' + str(i) + '个sample预测结果：', p_list.index(max(p_list)), '   真实结果：', test_['y'][i])
+
+mox.file.copy_parallel(src_url='model_fashion', dst_url=args.train_url)