图像分类使用TensorFlow，值错误

Question

我是深度学习和张量流的新手，我正在尝试使用张量流创建一个图像分类器，它将对5类图像进行分类。我的训练数据集是25000张图像，测试数据集是5000张图像。下面是我的代码：

import os
from random import shuffle

import cv2
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
import tflearn
from tflearn.layers.conv import conv_2d, max_pool_2d
from tflearn.layers.core import input_data, dropout, fully_connected
from tflearn.layers.estimator import regression
from tqdm import tqdm
import keras
from keras.models import save_model
"""from keras.models import Sequential
from keras.layers import Dense"""

TRAIN_DIR = 'train'
TEST_DIR = 'test'
IMG_SIZE = 16
LR = 1e-3
MODEL_NAME = 'cifar 10 -convnet'

def create_label(image_name):
    """ Create an one-hot encoded vector from image name """
    word_label = image_name.split('.')[0:2]
    if word_label == 'cat':
        return np.array([1,0,0,0,0])
    elif word_label == 'Dog':
        return np.array([0,1,0,0,0])
    elif word_label == 'Automobile':
        return np.array([0,0,1,0,0])
    elif word_label == 'Airplane':
        return np.array([0,0,0,1,0])
    elif word_label == 'Ship':
        return np.array([0,0,0,0,1])


def create_train_data():
    training_data = []
    for img in tqdm(os.listdir(TRAIN_DIR)):
        path = os.path.join(TRAIN_DIR, img)
        img_data = cv2.imread(path, cv2.IMREAD_GRAYSCALE)
        img_data = cv2.resize(img_data, (IMG_SIZE, IMG_SIZE))
        training_data.append([np.array(img_data), create_label(img)])
    shuffle(training_data)
    np.save('train_data.npy', training_data)
    return training_data


def create_test_data():
    testing_data = []
    for img in tqdm(os.listdir(TEST_DIR)):
        path = os.path.join(TEST_DIR, img)
        img_num = img.split('.')[0:2]
        img_data = cv2.imread(path, cv2.IMREAD_GRAYSCALE)
        img_data = cv2.resize(img_data, (IMG_SIZE, IMG_SIZE))
        testing_data.append([np.array(img_data), img_num])

    shuffle(testing_data)
    np.save('test_data.npy', testing_data)
    return testing_data


# If dataset is not created:
train_data = create_train_data()
test_data = create_test_data()
# If you have already created the dataset:
# train_data = np.load('train_data.npy')
# test_data = np.load('test_data.npy')
train = train_data[:25000]
test = train_data[:5000]
X_train = np.array([i[0] for i in train]).reshape(-1, IMG_SIZE, IMG_SIZE, 1)
y_train = [i[1] for i in train]
X_test = np.array([i[0] for i in test]).reshape(-1, IMG_SIZE, IMG_SIZE, 1)
y_test = [i[1] for i in test]

# Building The Model

tf.reset_default_graph()
convnet = input_data(shape=[None, IMG_SIZE, IMG_SIZE, 1], name='input')
convnet = conv_2d(convnet, 32, 5, activation='relu')
convnet = max_pool_2d(convnet, 5)
convnet = conv_2d(convnet, 64, 5, activation='relu')
convnet = max_pool_2d(convnet, 5)
convnet = conv_2d(convnet, 128, 5, activation='relu')
convnet = max_pool_2d(convnet, 5)
convnet = conv_2d(convnet, 64, 5, activation='relu')
convnet = max_pool_2d(convnet, 5)
convnet = conv_2d(convnet, 32, 5, activation='relu')
convnet = max_pool_2d(convnet, 5)
convnet = fully_connected(convnet, 1024, activation='relu')
convnet = dropout(convnet, 0.8)
convnet = fully_connected(convnet, 2, activation='softmax')
convnet = regression(convnet, optimizer='adam', learning_rate=LR, loss='categorical_crossentropy', name='targets')
model = tflearn.DNN(convnet, tensorboard_dir='log', tensorboard_verbose=0)
history = model.fit({'input': X_train}, {'targets': y_train}, n_epoch=25,
          validation_set=({'input': X_test}, {'targets': y_test}),
          snapshot_step=500, show_metric=True, run_id=MODEL_NAME)

#print(history.history.keys())
#plt.figure(1)

# summarize history for accuracy

"""plt.subplot(211)
plt.plot(history.history['acc'])
plt.plot(history.history['val_acc'])
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'test1'], loc='upper left')
plt.show()"""

fig = plt.figure(figsize=(25, 12))

for num, data in enumerate(test_data[:25]):

    img_num = data[1]
    img_data = data[0]

    y = fig.add_subplot(5, 5, num + 1)
    orig = img_data
    data = img_data.reshape(IMG_SIZE, IMG_SIZE, 1)
    model_out = model.predict([data])[0]

    if np.argmax(model_out) == 1:
        str_label = 'Dog'
    if np.argmax(model_out) == 2:
        str_label = 'Automobile'
    if np.argmax(model_out) == 3:
        str_label = 'Airplane'
    if np.argmax(model_out) == 4:
        str_label = 'Ship'
    else:
        str_label = 'Cat'

    y.imshow(orig, cmap='gray')
    plt.title(str_label)
    y.axes.get_xaxis().set_visible(False)
    y.axes.get_yaxis().set_visible(False)
plt.show()

我得到以下错误：ValueError：无法为Tensor'target / Y：0'提供形状值（64，），其形状为'（？，2）'

有人可以帮我弄这个吗？提前致谢。

Answer 1

问题在于标签的形状，因此请检查y_train和y_test的形状。它们都必须具有类似于模型输出的形状，它在错误...which has shape '(?, 2)'中显示，并在您创建最后一个完全连接的层convnet = fully_connected(convnet, 2, activation='softmax') 的行中定义。