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

【问题标题】：TensorFlow/TFLearn: ValueError: Cannot feed value of shape (64,10) for Tensor u'target/Y:0', which has shape '(?, 2)'TensorFlow / TFLearn：ValueError：无法为Tensor u'target / Y：0'提供形状（64,10）的值，其形状为'（？，2）'
【发布时间】：2017-12-11 13:16:18
【问题描述】：

我一直在尝试使用 TFLearn 训练数据集以实现卷积神经网络。我有一个包含 10 个类别的数据集，图像大小为 64*32，3 个输入通道和 2 个输出通道，即检测到/未检测到图像。

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: 无法为形状为“(?, 2)”的张量“TargetsData/Y:0”提供形状 (64, 10) 的值

我有 X 和 X_test 与图像参数和 Y 和 Y_test 与泡菜文件中的标签。我已经尝试过类似问题的解决方案，但对我不起作用。

如有任何帮助，将不胜感激。

谢谢。

【问题讨论】：

标签： python tensorflow deep-learning tflearn

【解决方案1】：

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

【讨论】：

【解决方案2】：

您收到该错误是因为您输入的形状与 tensorflow 的预期形状不匹配。要解决此问题，您可能需要将当前形状为 (64,10) 的 Y 重塑为 (?, 2)。例如，您将执行以下操作：

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

【讨论】：