Question

我正在努力创建一个可以区分猫和狗的图像分类器。我有以下代码：

import cv2
import os
from tqdm import tqdm
import numpy as np
import tensorflow as tf
img_height = 128
img_width = 128

path = "./train"
# class info
file = os.listdir(path)
index = []
images = []

# image size and channels
channels = 3
n_inputs = img_width * img_height * channels

# First convolutional layer
conv1_fmaps = 96  # Number of feature maps created by this layer
conv1_ksize = 4  # kernel size 3x3
conv1_stride = 2
conv1_pad = "SAME"

# Second convolutional layer
conv2_fmaps = 192
conv2_ksize = 4
conv2_stride = 4
conv2_pad = "SAME"

# Third layer is a pooling layer
pool3_fmaps = conv2_fmaps  # Isn't it obvious?

n_fc1 = 192  # Total number of output features
n_outputs = 2

with tf.name_scope("inputs"):
    X = tf.placeholder(tf.float32, shape=[None, img_width, img_height, channels], name="X")
    X_reshaped = tf.reshape(X, shape=[-1, img_height, img_width, channels])
    y = tf.placeholder(tf.int32, shape=[None, 2], name="y")
conv1 = tf.layers.conv2d(X_reshaped, filters=conv1_fmaps,  kernel_size=conv1_ksize, strides=conv1_stride, padding=conv1_pad,     activation=tf.nn.relu, name="conv1")
conv2 = tf.layers.conv2d(conv1, filters=conv2_fmaps, kernel_size=conv2_ksize,         strides=conv2_stride, padding=conv2_pad, activation=tf.nn.relu, name="conv2")

n_epochs = 10
batch_size = 250

with tf.name_scope("pool3"):
    pool3 = tf.nn.max_pool(conv2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding="VALID")
    pool3_flat = tf.reshape(pool3, shape=[-1, pool3_fmaps * 8 * 8])

with tf.name_scope("fc1"):
    fc1 = tf.layers.dense(pool3_flat, n_fc1, activation=tf.nn.relu name="fc1")
with tf.name_scope("output"):
    logits = tf.layers.dense(fc1, n_outputs, name="output")
    Y_proba = tf.nn.softmax(logits, name="Y_proba")

with tf.name_scope("train"):
    xentropy=tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits,      labels=y)    
    loss = tf.reduce_mean(xentropy)
    optimizer = tf.train.AdamOptimizer()
    training_op = optimizer.minimize(loss)

with tf.name_scope("eval"):
    correct = tf.nn.in_top_k(logits, y, 1)
    accuracy = tf.reduce_mean(tf.cast(correct, tf.float32))

init = tf.global_variables_initializer()

with tf.name_scope("init_and_save"):
    saver = tf.train.Saver()


def next_batch(num):
    index = []
    images = []
# Data set Creation
    print("Creating batch dataset "+str(num+1)+"...")
    for f in tqdm(range(num * batch_size, (num+1)*batch_size)):
        if file[f].find("dog"):
            index.append(np.array([0, 1]))
        else:
           index.append(np.array([1, 0]))
           image = cv2.imread(path + "/" + file[f])
           image = cv2.resize(image, (img_width, img_height), 0, 0, cv2.INTER_LINEAR)
        # image = image.astype(np.float32)
        images.append(image)

    images = np.array(images, dtype=np.uint8)
    images = images.astype('float32')
    images = images / 255

    print("\nBatch "+str(num+1)+" creation finished.")
    # print([images, index])
    return [images, index]

with tf.Session() as sess:
    init.run()
    for epoch in range(n_epochs):
        for iteration in range(25000 // batch_size):
            X_batch, y_batch = next_batch(iteration)
            sess.run(training_op, feed_dict={X: X_batch, y: y_batch})
            acc_train = accuracy.eval(feed_dict={X: X_batch, y: y_batch})
            print(epoch, "Train accuracy:", acc_train)
        save_path = saver.save(sess, "./dogvscat_mnist_model.ckpt")

但是我收到了这个错误：

ValueError：排名不匹配：标签排名（收到2）应该等于logits排名减1（收到2）。

任何人都可以指出问题并帮助我解决问题。我对此完全陌生。

Answer 1

对于tf.nn.sparse_softmax_corss_entropy_with_logits rank(labels) = rank(logits) - 1，您需要重新定义标签占位符，如下所示

...
y = tf.placeholder(tf.int32, shape=[None], name="y")
...
xentropy=tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits,labels=y)    
...
X_batch, y_batch = next_batch(iteration)
y_batch = np.argmax(y_batch, axis=1)

或者您可以在不更改标签占位符的情况下使用tf.nn.softmax_cross_entropy_with_logits。

xentropy=tf.nn.softmax_cross_entropy_with_logits(logits=logits,labels=y)

Tensorflow中的排名不匹配错误

1 个答案: