python - TensorFlow 模型获得零损失

Question

import tensorflow as tf
import numpy as np
import os
import re
import PIL


def read_image_label_list(img_directory, folder_name):
    # Input:
    #   -Name of folder (test\\\\train)
    # Output:
    #   -List of names of files in folder
    #   -Label associated with each file

    cat_label = 1
    dog_label = 0
    filenames = []
    labels = []

    dir_list = os.listdir(os.path.join(img_directory, folder_name))  # List of all image names in 'folder_name' folder

    # Loop through all images in directory
    for i, d in enumerate(dir_list):
        if re.search("train", folder_name):
            if re.search("cat", d):  # If image filename contains 'Cat', then true
                labels.append(cat_label)
            else:
                labels.append(dog_label)
        filenames.append(os.path.join(img_dir, folder_name, d))

    return filenames, labels


# Define convolutional layer
def conv_layer(input, channels_in, channels_out):
    w_1 = tf.get_variable("weight_conv", [5,5, channels_in, channels_out], initializer=tf.contrib.layers.xavier_initializer())
    b_1 = tf.get_variable("bias_conv", [channels_out], initializer=tf.zeros_initializer())
    conv = tf.nn.conv2d(input, w_1, strides=[1,1,1,1], padding="SAME")
    activation = tf.nn.relu(conv + b_1)
    return activation


# Define fully connected layer
def fc_layer(input, channels_in, channels_out):
    w_2 = tf.get_variable("weight_fc", [channels_in, channels_out], initializer=tf.contrib.layers.xavier_initializer())
    b_2 = tf.get_variable("bias_fc", [channels_out], initializer=tf.zeros_initializer())
    activation = tf.nn.relu(tf.matmul(input, w_2) + b_2)
    return activation


# Define parse function to make input data to decode image into
def _parse_function(img_path, label):
    img_file = tf.read_file(img_path)
    img_decoded = tf.image.decode_image(img_file, channels=3)
    img_decoded.set_shape([None,None,3])
    img_decoded = tf.image.resize_images(img_decoded, (28, 28), method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)
    img_decoded = tf.image.per_image_standardization(img_decoded)
    img_decoded = tf.cast(img_decoded, dty=tf.float32)
    label = tf.one_hot(label, 1)
    return img_decoded, label


tf.reset_default_graph()

# Define parameterspe
EPOCHS = 10
BATCH_SIZE_training = 64
learning_rate = 0.001
img_dir = 'C:/Users/tharu/PycharmProjects/cat_vs_dog/data'
batch_size = 128

# Define data
features, labels = read_image_label_list(img_dir, "train")

# Define dataset
dataset = tf.data.Dataset.from_tensor_slices((features, labels))  # Takes slices in 0th dimension
dataset = dataset.map(_parse_function)
dataset = dataset.batch(batch_size)
iterator = dataset.make_initializable_iterator()

# Get next batch of data from iterator
x, y = iterator.get_next()

# Create the network (use different variable scopes for reuse of variables)
with tf.variable_scope("conv1"):
    conv_1 = conv_layer(x, 3, 32)
    pool_1 = tf.nn.max_pool(conv_1, ksize=[1,2,2,1], strides=[1,2,2,1], padding="SAME")

with tf.variable_scope("conv2"):
    conv_2 = conv_layer(pool_1, 32, 64)
    pool_2 = tf.nn.max_pool(conv_2, ksize=[1,2,2,1], strides=[1,2,2,1], padding="SAME")
    flattened = tf.contrib.layers.flatten(pool_2)

with tf.variable_scope("fc1"):
    fc_1 = fc_layer(flattened, 7*7*64, 1024)
with tf.variable_scope("fc2"):
    logits = fc_layer(fc_1, 1024, 1)


# Define loss function
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=tf.cast(y, dtype=tf.int32)))

# Define optimizer
train = tf.train.AdamOptimizer(learning_rate).minimize(loss)


with tf.Session() as sess:
    # Initiliaze all the variables
    sess.run(tf.global_variables_initializer())

    # Train the network
    for i in range(EPOCHS):
        # Initialize iterator so that it starts at beginning of training set for each epoch
        sess.run(iterator.initializer)
        print("EPOCH", i)
        while True:
            try:
                _, epoch_loss = sess.run([train, loss])

            except tf.errors.OutOfRangeError:  # Error given when out of data
                if i % 2 == 0:
                    # [train_accuaracy] = sess.run([accuracy])
                    # print("Step ", i, "training accuracy = %{}".format(train_accuaracy))
                    print(epoch_loss)
                break

我花了几个小时试图系统地弄清楚为什么我在运行这个模型时一直得到 0 损失。

• 特征 = 每个图像的文件位置列表(例如 ['\data\train\cat.0.jpg',/data\train\cat.1.jpg])
• Labels = [Batch_size, 1] one_hot 向量

起初我以为是因为我的数据有问题。但我在调整大小后查看了数据，图像看起来不错。

然后我尝试了几个不同的损失函数，因为我想我可能误解了 tensorflow 函数 softmax_cross_entropy 的作用，但这并没有解决任何问题。

我试过只运行“logits”部分来查看输出是什么。这只是一个小样本，我觉得这些数字还不错:

 [[0.06388957]
 [0.        ]
 [0.16969752]
 [0.24913025]
 [0.09961276]]

softmax_cross_entropy 函数肯定能够计算出相应标签为 0 或 1 的损失？我不确定我是否遗漏了什么。任何帮助将不胜感激。

Answer 1

作为documented :

logits and labels must have the same shape, e.g. [batch_size, num_classes] and the same dtype (either float16, float32, or float64).

由于您提到您的标签是“[Batch_size, 1] one_hot vector”，我假设您的 logits 和 labels 都是 [Batch_size, 1] 形状。这肯定会导致零损失。从概念上讲，您只有 1 个类 (num_classes=1)，并且您不会错 (loss=0)。

所以至少对于你的 labels，你应该转换它:tf.one_hot(indices=labels, depth=num_classes)。您的预测 logits 也应该具有形状 [batch_size, num_classes] 输出。

或者，您可以使用 sparse_softmax_cross_entropy_with_logits ，其中:

A common use case is to have logits of shape [batch_size, num_classes] and labels of shape [batch_size]. But higher dimensions are supported.