TensorFlow/TFLearn: ValueError:不能为张量u'target/Y:0'，它有形状的形状(64,10)提供的输入值。2)'

I have been trying to train a dataset using TFLearn to implement a convolutional neural network. I have a dataset of 10 classes with image size is 64*32, 3 channels of input and 2 outputs i.e image detected/not detected.

我一直在尝试用TFLearn来训练一个数据集来实现卷积神经网络。我有一个10个类的数据集，图像大小是64*32,3个输入通道和2个输出I。e图像检测/检测。

Here is my code.

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!")

I get the following error

我得到了下面的错误。

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

ValueError:无法为张量“TargetsData/Y:0”提供形状的值(64,10)，它的形状是(?2)'

I have X and X_test with parameters of images and Y and Y_test with labeles in the pickle file. I have tried solutions from similar question, but the didn't work for me.

我有X和X_test参数的图像和Y和Y_test与labeles在pickle文件。我尝试过类似的问题，但对我不起作用。

Any help would be appericiated.

任何帮助都将被接受。

Thanks.

谢谢。

2 个解决方案

#1

You are getting that error because there is a mismatch between the shape of what you are feeding and what the tensorflow is expecting. To fix the issue, you might want to reshape your Y which is currently shaped at (64,10) to (?, 2). For example, you would do the following:

你会得到这个错误，因为你所吃的东西的形状和你所期望的东西之间不匹配。为了解决这个问题，你可能想要重塑你的Y，它目前的形状是(64,10)到(?，例如，你会做以下工作:

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

#2

Youve specified your output tensor shape as (?,2) and your labels is of the shape (?,10). Your label and output tensor shape must be the same.

您已经指定了输出张量形状为(?，2)，您的标签是形状(?，10)。你的标签和输出张量的形状必须相同。