TensorFlow/TFLearn：ValueError：无法为张量u'target/Y：0'提供shape（64,10）的值，其形状为'（？，2）'

我一直在尝试使用TFLearn来训练数据集实现卷积神经网络。我有一个10级的数据集，图像大小为64 * 32,3个通道的输入和2个输出，即图像检测/未检测。TensorFlow/TFLearn：ValueError：无法为张量u'target/Y：0'提供shape（64,10）的值，其形状为'（？，2）'

这是我的代码。

# Load the data set 
def read_data(): 
    with open("deep_logo.pickle", 'rb') as f: 
     save = pickle.load(f) 
     X = save['train_dataset'] 
     Y = save['train_labels'] 
     X_test = save['test_dataset'] 
     Y_test = save['test_labels'] 
     del save 

    return [X, X_test], [Y, Y_test] 

def reformat(dataset, labels): 
    dataset = dataset.reshape((-1, 64, 32,3)).astype(np.float32) 
    labels = (np.arange(10) == labels[:, None]).astype(np.float32) 
    return dataset, labels 

dataset, labels = read_data() 
X,Y = reformat(dataset[0], labels[0]) 
X_test, Y_test = reformat(dataset[2], labels[2]) 
print('Training set', X.shape, Y.shape) 
print('Test set', X_test.shape, Y_test.shape)    

#building convolutional layers 

network = input_data(shape=[None, 64, 32, 3],data_preprocessing=img_prep,    
data_augmentation=img_aug) 

network = conv_2d(network, 32, 3, activation='relu') 

network = max_pool_2d(network, 2) 

network = conv_2d(network, 64, 3, activation='relu') 

network = conv_2d(network, 128, 3, activation='relu') 

network = max_pool_2d(network, 2) 

network = fully_connected(network, 512, activation='relu') 

network = dropout(network, 0.5) 

# Step 8: Fully-connected neural network with two outputs to make the final 
prediction 
network = fully_connected(network, 2, activation='softmax') 

network = regression(network, optimizer='adam', 
        loss='categorical_crossentropy', 
        learning_rate=0.001) 

# Wrap the network in a model object 
model = tflearn.DNN(network, tensorboard_verbose=0, checkpoint_path='logo- 
classifier.tfl.ckpt') 

# Training it . 100 training passes and monitor it as it goes. 
model.fit(X,Y, n_epoch=100, shuffle=True, validation_set=(X_test, Y_test), 
      show_metric=True, batch_size=64, 
      snapshot_epoch=True, 
      run_id='logo-classifier') 

# Save model when training is complete to a file 
model.save("logo-classifier.tfl") 
print("Network trained and saved as logo-classifier.tfl!")

我收到以下错误

ValueError: Cannot feed value of shape (64, 10) for Tensor 'TargetsData/Y:0', which has shape '(?, 2)'

我有X和X_test用图像和Y Y_test的参数与泡菜文件labeles。我已经尝试了类似问题的解决方案，但是这对我并不适用。

任何帮助将被appericiated。

谢谢。

来源

2017-07-07 Achmn

您已将输出张量形状指定为（？，2），并且您的标签具有形状（？，10）。您的标签和输出张量形状必须相同。

来源

2017-07-07 10:25:01

您正在收到该错误，因为您正在馈送的形状与张量流所期待的内容不匹配。为了解决这个问题，你可能需要重塑你的Y（目前形状为（64,10）到（？，2））。例如，您可以执行以下操作：

Y = np.reshape(Y, (-1, 2))

来源

2017-08-30 17:29:35 Heapify

TensorFlow/TFLearn：ValueError：无法为张量u'target/Y：0'提供shape（64,10）的值，其形状为'（？，2）'

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