Question

我一直在尝试使用TFLearn训练数据集来实现卷积神经网络。我有一个10类的数据集，图像大小为64 * 32,3个输入通道和2个输出，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Tensor'TargetsData / Y：0'提供形状值（64,10），其形状为'（？，2）'

我有X和X_test包含图像参数，Y和Y_test包含pickle文件中的labeles。我尝试过类似问题的解决方案，但这对我没用。

任何帮助都会被暗示。

感谢。

Answer 1

您正在收到该错误，因为您正在喂食的形状与张量流所期望的形状之间存在不匹配。要解决此问题，您可能需要将当前形状为（64,10）的Y重新整形为（？，2）。例如，您将执行以下操作：

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

Answer 2

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。

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

2 个答案: