使用CIFAR-10樣本數據集測試卷積神經網絡(ConvolutionalNeuralNetwork,CNN)

第一次將例程跑起來了，有些興趣。

參考的是以下URL：

http://www.yidianzixun.com/article/0KNz7OX1

原本是比較Keras和Tensorflow的，我如今的水平，只能是跑通一個算一個啦。

由於要比較CPU和GPU的性能，兩個DOCKER以下：

tensorflow/tensorflow:1.12.0-gpu-py3

tensorflow/tensorflow:1.12.0-py3

CIFAR-10的數據自已從網上下載，因此出現以下錯誤時，要本身更改爲一個內網URL地址：

Traceback (most recent call last): File "train_network_tf.py", line 26, in <module>
    split = tf.keras.datasets.cifar10.load_data() File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/keras/datasets/cifar10.py", line 40, in load_data path = get_file(dirname, origin=origin, untar=True) File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/keras/utils/data_utils.py", line 251, in get_file raise Exception(error_msg.format(origin, e.errno, e.reason)) Exception: URL fetch failure on https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz: None -- [Errno -3] Temporary failure in name resolution

/usr/local/lib/python3.5/dist-packages/tensorflow/python/keras/datasets/cifar10.py這個文件40行是一個網址，更改一下就OK了。
一，modle層實現代碼
pyimagesearch/minivggnettf.py

# import the necessary packages
import tensorflow as tf class MiniVGGNetTF: @staticmethod def build(width, height, depth, classes): # initialize the input shape and channel dimension, assuming
        # TensorFlow/channels-last ordering
        inputShape = (height, width, depth) chanDim = -1

        # define the model input
        inputs = tf.keras.layers.Input(shape=inputShape) # first (CONV => RELU) * 2 => POOL layer set
        x = tf.keras.layers.Conv2D(32, (3, 3), padding="same")(inputs) x = tf.keras.layers.Activation("relu")(x) x = tf.keras.layers.BatchNormalization(axis=chanDim)(x) x = tf.keras.layers.Conv2D(32, (3, 3), padding="same")(x) x = tf.keras.layers.Lambda(lambda t: tf.nn.crelu(x))(x) x = tf.keras.layers.BatchNormalization(axis=chanDim)(x) x = tf.keras.layers.MaxPooling2D(pool_size=(2, 2))(x) x = tf.keras.layers.Dropout(0.25)(x) # second (CONV => RELU) * 2 => POOL layer set
        x = tf.keras.layers.Conv2D(64, (3, 3), padding="same")(x) x = tf.keras.layers.Lambda(lambda t: tf.nn.crelu(x))(x) x = tf.keras.layers.BatchNormalization(axis=chanDim)(x) x = tf.keras.layers.Conv2D(64, (3, 3), padding="same")(x) x = tf.keras.layers.Lambda(lambda t: tf.nn.crelu(x))(x) x = tf.keras.layers.BatchNormalization(axis=chanDim)(x) x = tf.keras.layers.MaxPooling2D(pool_size=(2, 2))(x) x = tf.keras.layers.Dropout(0.25)(x) # first (and only) set of FC => RELU layers
        x = tf.keras.layers.Flatten()(x) x = tf.keras.layers.Dense(512)(x) x = tf.keras.layers.Lambda(lambda t: tf.nn.crelu(x))(x) x = tf.keras.layers.BatchNormalization()(x) x = tf.keras.layers.Dropout(0.5)(x) # softmax classifier
        x = tf.keras.layers.Dense(classes)(x) x = tf.keras.layers.Activation("softmax")(x) # create the model
        model = tf.keras.models.Model(inputs, x, name="minivggnet_tf") # return the constructed network architecture
        return model

二，數據訓練

train_network_tf.py

# USAGE # python train_network_tf.py

# set the matplotlib backend so figures can be saved in the background
import matplotlib matplotlib.use("Agg") # import the necessary packages
from pyimagesearch.minivggnettf import MiniVGGNetTF from sklearn.preprocessing import LabelBinarizer from sklearn.metrics import classification_report import matplotlib.pyplot as plt import tensorflow as tf import numpy as np import argparse # construct the argument parser and parse the arguments
ap = argparse.ArgumentParser() ap.add_argument("-p", "--plot", type=str, default="plot_tf.png", help="path to output loss/accuracy plot") args = vars(ap.parse_args()) # load the training and testing data, then scale it into the # range [0, 1]
print("[INFO] loading CIFAR-10 data...") split = tf.keras.datasets.cifar10.load_data() ((trainX, trainY), (testX, testY)) = split trainX = trainX.astype("float") / 255.0 testX = testX.astype("float") / 255.0

# convert the labels from integers to vectors
lb = LabelBinarizer() trainY = lb.fit_transform(trainY) testY = lb.transform(testY) # initialize the label names for the CIFAR-10 dataset
labelNames = ["airplane", "automobile", "bird", "cat", "deer", "dog", "frog", "horse", "ship", "truck"] # initialize the initial learning rate, total number of epochs to # train for, and batch size
INIT_LR = 0.01 EPOCHS = 5 BS = 32

# initialize the optimizer and model
print("[INFO] compiling model...") opt = tf.keras.optimizers.SGD(lr=INIT_LR, decay=INIT_LR / EPOCHS) model = MiniVGGNetTF.build(width=32, height=32, depth=3, classes=len(labelNames)) model.compile(loss="categorical_crossentropy", optimizer=opt, metrics=["accuracy"]) # train the network
print("[INFO] training network for {} epochs...".format(EPOCHS)) H = model.fit(trainX, trainY, validation_data=(testX, testY), batch_size=BS, epochs=EPOCHS, verbose=1) # evaluate the network
print("[INFO] evaluating network...") predictions = model.predict(testX, batch_size=32) print(classification_report(testY.argmax(axis=1), predictions.argmax(axis=1), target_names=labelNames)) # plot the training loss and accuracy
plt.style.use("ggplot") plt.figure() plt.plot(np.arange(0, EPOCHS), H.history["loss"], label="train_loss") plt.plot(np.arange(0, EPOCHS), H.history["val_loss"], label="val_loss") plt.plot(np.arange(0, EPOCHS), H.history["acc"], label="train_acc") plt.plot(np.arange(0, EPOCHS), H.history["val_acc"], label="val_acc") plt.title("Training Loss and Accuracy on Dataset") plt.xlabel("Epoch #") plt.ylabel("Loss/Accuracy") plt.legend(loc="lower left") plt.savefig(args["plot"])

三，結果對比：

當使用GPU吧，一個批次完成須要30秒上下。

而只使用CPU的話，一個批次完成則須要330秒以上。

效率提升10倍以上啊。