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当我运行这个模型时我一直得到 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]）
标签 = [Batch_size, 1] one_hot 矢量

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。

我已经尝试运行只查看 'logits' 部分以查看输出结果。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]]

那么 softmax_cross_entropy 函数应该能够计算这个损失，因为相应的标签是 0 或 1？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).

因为你提到你的标签是“[Batch_size, 1] one_hot vector”，我假设你的 logits 和 labels 都是 [Batch_size, 1] 形状。这肯定会导致零损失。从概念上讲，你只有 1 class (num_classes=1)，你不会错 (loss=0)。

所以至少对你来说labels，你应该改造它：tf.one_hot(indices=labels, depth=num_classes)。你的预测 logits 也应该有一个形状 [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.

TensorFlow 模型获得零损失

TensorFlow model gets zero loss

python

tensorflow

cross-entropy

convolutional-neural-network