tf.kerasr入門示例:Lenet手寫字元分類(2擴充) eager模式下Sequence生成器方式加載資料并訓練

• Sequence方式擴充自己的自定義資料集加載, 參考連結: https://blog.csdn.net/m0_37477175/article/details/79716312
• eager模式下使用tf.keras訓練和評估方式

源代碼

# -*- coding: utf-8 -*-
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import os
import time
import argparse
import math

import tensorflow as tf
from tensorflow.keras.utils import Sequence
from tensorflow.data import Dataset
from tensorflow.contrib.eager.python import datasets
import tensorflow.contrib.eager as tfe
import numpy as np

"""兩種方式加載的資料集不同圖像部分資料是不同的,
official.mnist： 加載的圖像是uint8資料類型編碼, /255. 需要歸一化
tensorflow.examples.tutorials.mnist 是float類型編碼, 無需歸一化操作
"""
from tensorflow.keras.datasets import mnist
from tensorflow.keras.utils import to_categorical
from tensorflow.keras.models import Model
from tensorflow.keras.layers import MaxPooling2D, Conv2D, Input, Flatten, Dense, Dropout

# 立即執行模式
tf.enable_eager_execution()

"""
Sequence生成器的方法
__iter__()
__getitem__(index)
支援batch
"""
class DataGenerator(Sequence):
    # 自定義資料集加載方式, 
    # 傳入資料可以是檔案清單或是其他格式,實作相應的加載和預處理方法即可
    def __init__(self, x, y, batch_size=, shuffle=True):
        self.batch_size = batch_size
        self.x, self.y = x, y
        # 索引重排
        self.indexes = np.arange(len(self.x))
        self.shuffle = shuffle

    def __len__(self):
        # 計算每一個epoch的疊代次數
        return math.ceil(len(self.x) / float(self.batch_size))

    def __getitem__(self, index):
        # 生成每個batch資料，這裡就根據自己對資料的讀取方式進行發揮了
        # 生成batch_size個索引
        batch_indexs = self.indexes[index*self.batch_size:(index+)*self.batch_size]
        # 根據索引擷取資料集合中的資料
        x, y = self.data_generation(batch_indexs)
        return x, y

    def on_epoch_end(self):
        # 在每一次epoch結束是否需要進行一次随機，重新随機一下index
        if self.shuffle == True:
            np.random.shuffle(self.indexes)

    def data_generation(self, batch_indexs):
        x = self.x[batch_indexs]
        y = self.y[batch_indexs]
        return x, y

class LeNet(Model):
    def __init__(self, input_shape=(, , ), num_classes=):
        # super(LeNet, self).__init__(name="LeNet")
        self.num_classes = num_classes
        ''' 定義要用到的層 layers '''
        # 輸入層
        img_input = Input(shape=input_shape)

        # Conv => ReLu => Pool
        x = Conv2D(filters=, kernel_size=, padding="same", activation="relu" ,name='block1_conv1')(img_input)
        x = MaxPooling2D(pool_size=(, ), strides=(, ), name='block1_pool')(x)
        # Conv => ReLu => Pool
        x = Conv2D(filters=, kernel_size=, padding="same", activation="relu", name='block1_conv2')(x)
        x = MaxPooling2D(pool_size=(, ), strides=(, ), name='block1_poo2')(x)
        # 壓成一維
        x = Flatten(name='flatten')(x)
        # 全連接配接層
        x = Dense(units=, activation="relu", name="f1")(x)
        # softmax分類器
        x = Dense(units=num_classes, activation="softmax", name="prediction")(x)

        # 調用Model類的Model(input, output, name="***")構造方法
        super(LeNet, self).__init__(img_input, x, name="LeNet")

# 自定義損失函數
def loss(logits, labels):
    # softmax_cross_entropy_with_logits 為每一個輸入項結果計算一個損失, 傳回值arrayList, 長度N=Batch
    # reduce_mean 再求均值
    return tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(labels=labels, logits=logits))
    # return tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(labels=labels, logits=logits))

# 自定義評估函數
def compute_accuracy(logits, labels):
    predictions = tf.argmax(input=logits, axis=, output_type=tf.int64)
    labels = tf.argmax(input=labels, axis=, output_type=tf.int64) #tf.cast(x=labels, dtype=tf.int64)
    batch_size = int(logits.shape[])
    return tf.reduce_sum(tf.cast(tf.equal(predictions, labels), dtype=tf.float32)) / batch_size



def run_mnist_eager(cfg):
    # 自動選擇裝置
    (device, data_format) = ('/gpu:0', 'channels_first')
    if not tf.test.is_gpu_available():
        (device, data_format) = ('/cpu:0', 'channels_last')

    print('Using device %s, and data format %s.' % (device, data_format))
    # 載入資料集
    train_ds, test_ds = load_mnist() # shape = (?, 768) / (?)
    # train_ds = train_ds.shuffle(60000, reshuffle_each_iteration=True).batch(cfg.batch_size)
    # test_ds = test_ds.batch(cfg.batch_size)
    # print(train_ds.output_shapes, test_ds.output_shapes)

    # 建立 model and optimizer
    model = LeNet()
    optimizer = tf.train.MomentumOptimizer(cfg.lr, cfg.momentum)
    model.compile(optimizer=optimizer,
                  loss=loss, #'categorical_crossentropy',
                  metrics=[compute_accuracy] ) #['accuracy']

    # Create and restore checkpoint (if one exists on the path)
    checkpoint_prefix = os.path.join(cfg.model_dir, 'ckpt')
    step_counter = tf.train.get_or_create_global_step()
    checkpoint = tfe.Checkpoint(model=model, optimizer=optimizer, step_counter=step_counter)
    # 從檢查點檔案恢複模型參數, 如果檔案存在.
    checkpoint.restore(tf.train.latest_checkpoint(cfg.model_dir))

    # Generator 使用生成器方式提供資料,支援eager模式
    train_ds = DataGenerator(train_ds[], train_ds[], batch_size=)
    test_ds = DataGenerator(test_ds[], test_ds[], batch_size=)
    # Train and evaluate for a set number of epochs.
    with tf.device(device): # 使用GPU必須有此一句
        for _ in range(cfg.train_epochs):
            start = time.time()

            model.fit_generator(generator=train_ds, epochs=)
            # model.fit(train_ds[0], train_ds[1], batch_size=200, epochs=1)
            # verbose=0 不顯示
            # 生成器
            # _loss, _acc = model.evaluate(test_ds[0], test_ds[1], batch_size=100, verbose=0)
            # _loss, _acc = model.evaluate_generator(generator=test_ds, steps=10000)
            #

            _loss, _acc = model.evaluate_generator(generator=test_ds, verbose=)
            print("test dataset loss: %f acc: %f" % (_loss, _acc))
            # train(model, optimizer, train_ds, step_counter, cfg.log_interval)
            end = time.time()
            print('\nTrain time for epoch #%d (%d total steps): %f' %
                    (checkpoint.save_counter.numpy() + , step_counter.numpy(), end - start))

            # test(model, test_ds)
            checkpoint.save(checkpoint_prefix)

def arg_parse():
    """參數定義"""
    parser = argparse.ArgumentParser(description="Lenet-5 MNIST 模型")
    parser.add_argument("--lr", dest="lr", help="學習率", default=, type=float)
    parser.add_argument("--momentum", dest="momentum", help="SGD momentum.", default=)

    parser.add_argument("--data_dir", dest="data_dir", help="資料集下載下傳/儲存目錄", default="data/mnist/input_data/")
    parser.add_argument("--model_dir", dest="model_dir", help="模型儲存目錄", default="data/mnist/checkpoints/")
    parser.add_argument("--batch_size", dest="batch_size", help="訓練或測試時 Batch Size", default=, type=int)
    parser.add_argument("--train_epochs", dest="train_epochs", help="訓練時epoch疊代次數", default=, type=int)
    parser.add_argument("--log_interval", dest="log_interval", help="日志列印間隔", default=, type=int)

    # 傳回轉換好的結果
    return parser.parse_args()

def load_mnist():
    # 加載資料,轉換編碼格式并歸一化
    (x_train, y_train), (x_test, y_test) = mnist.load_data()
    x_train = x_train.astype("float32") / 
    x_test = x_test.astype("float32") / 

    # 擴充1維, 等效寫法
    x_train = x_train[:, :, :, None]
    x_test = x_test[:, :, :, None]

    print(x_train.shape, "train shape")
    print(x_test.shape, "test shape")

    y_train = to_categorical(y_train, )
    y_test = to_categorical(y_test, )
    train_ds = (x_train, y_train)
    test_ds = (x_test, y_test)

    return train_ds, test_ds

if __name__ == '__main__':
  args = arg_parse()
  run_mnist_eager(args)

           

運作結果

-- ::: I tensorflow/core/platform/cpu_feature_guard.cc:] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2018-09-10 16:50:42.035425: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1405] Found device 0 with properties: 
name: TITAN Xp major: 6 minor: 1 memoryClockRate(GHz): 1.582
pciBusID: 0000:85:00.0
totalMemory: 11.90GiB freeMemory: 11.74GiB
2018-09-10 16:50:42.035472: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1484] Adding visible gpu devices: 0
2018-09-10 16:50:42.494465: I tensorflow/core/common_runtime/gpu/gpu_device.cc:965] Device interconnect StreamExecutor with strength 1 edge matrix:
2018-09-10 16:50:42.494531: I tensorflow/core/common_runtime/gpu/gpu_device.cc:971]      0 
2018-09-10 16:50:42.494539: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] 0:   N 
2018-09-10 16:50:42.498417: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1097] Created TensorFlow device (/device:GPU:0 with 11355 MB memory) -> physical GPU (device: 0, name: TITAN Xp, pci bus id: 0000:85:00.0, compute capability: 6.1)
Using device /gpu:0, and data format channels_first.
(60000, 28, 28, 1) train shape
(10000, 28, 28, 1) test shape
2018-09-10 16:50:43.133449: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1484] Adding visible gpu devices: 0
2018-09-10 16:50:43.133544: I tensorflow/core/common_runtime/gpu/gpu_device.cc:965] Device interconnect StreamExecutor with strength 1 edge matrix:
2018-09-10 16:50:43.133554: I tensorflow/core/common_runtime/gpu/gpu_device.cc:971]      0 
2018-09-10 16:50:43.133578: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] 0:   N 
2018-09-10 16:50:43.133817: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1097] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 11355 MB memory) -> physical GPU (device: 0, name: TITAN Xp, pci bus id: 0000:85:00.0, compute capability: 6.1)
Epoch 1/1
  1/300 [..............................] - ETA: 6:29 - loss: 2.3625 - compute_accuracy: 0.0850
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300/300 [==============================] - 4s 15ms/step - loss: 2.3553 - compute_accuracy: 0.2668
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50/50 [==============================] - 0s 5ms/step
test dataset loss: 2.343393 acc: 0.370100

Train time for epoch #1 (0 total steps): 4.813967
Epoch 1/1
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300/300 [==============================] - 3s 10ms/step - loss: 2.1686 - compute_accuracy: 0.4046
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50/50 [==============================] - 0s 6ms/step
test dataset loss: 1.871826 acc: 0.650700

Train time for epoch #2 (0 total steps): 3.449475
Epoch 1/1
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300/300 [==============================] - 3s 11ms/step - loss: 1.7603 - compute_accuracy: 0.7491
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test dataset loss: 1.678678 acc: 0.812900

Train time for epoch #3 (0 total steps): 3.654161
Epoch 1/1
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300/300 [==============================] - 3s 11ms/step - loss: 1.6646 - compute_accuracy: 0.8195
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test dataset loss: 1.645696 acc: 0.831400

Train time for epoch #4 (0 total steps): 3.597641

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