ValueError：無法提供形狀的值TensorFlow

Question

我遇到TFLearn/TensorFlow的一些問題。我已經調整了我的np.reshape到合適的尺寸，但我與錯誤而崩潰：

這個錯誤發生在訓練代碼行17：

ValueError: Cannot feed value of shape (48, 1) for Tensor 'TargetsData/Y:0', which has shape '(?, 2)' 

線路供參考：

model.fit(X, Y, n_epoch=250, validation_set=(W,Z), show_metric=True) 

我的訓練代碼如下：

import deepneuralnet as net 
import numpy as np 
from tflearn.data_utils import image_preloader 
import os 

model = net.model 
train_path = os.path.abspath('train') 
print(train_path) 
X, Y = image_preloader(target_path=train_path, image_shape=(100, 100), 
mode='folder', grayscale=False, categorical_labels=True, normalize=True) 
X = np.reshape(X, (-1, 100, 100, 3)) 

validate_path = os.path.abspath('validate') 
W, Z = image_preloader(target_path=validate_path, image_shape=(100, 100), 
mode='folder', grayscale=False, categorical_labels=True, normalize=True) 
W = np.reshape(W, (-1, 100, 100, 3)) 
model.fit(X, Y, n_epoch=250, validation_set=(W,Z), show_metric=True) 
model.save('./ZtrainedNet/final-model.tfl') 

而神經網爲：

import tflearn 
from tflearn.layers.core import input_data, dropout, fully_connected 
from tflearn.layers.conv import conv_2d, max_pool_2d 
from tflearn.layers.estimator import regression 
from tflearn.metrics import Accuracy 

acc = Accuracy() 
network = input_data(shape=[None, 100, 100, 3]) 
# Conv layers ------------------------------------ 
network = conv_2d(network, 64, 3, strides=1, activation='relu') 
network = max_pool_2d(network, 2, strides=2) 
network = conv_2d(network, 64, 3, strides=1, activation='relu') 
network = max_pool_2d(network, 2, strides=2) 
network = conv_2d(network, 64, 3, strides=1, activation='relu') 
network = conv_2d(network, 64, 3, strides=1, activation='relu') 
network = conv_2d(network, 64, 3, strides=1, activation='relu') 
network = max_pool_2d(network, 2, strides=2) 
# Fully Connected Layers ------------------------- 
network = fully_connected(network, 1024, activation='tanh') 
network = dropout(network, 0.5) 
network = fully_connected(network, 1024, activation='tanh') 
network = dropout(network, 0.5) 
network = fully_connected(network, 2, activation='softmax') 
network = regression(network, optimizer='momentum', 
loss='categorical_crossentropy', 
learning_rate=0.001, metric=acc) 
model = tflearn.DNN(network) 

我的理解是它與softmax有關嗎？我不確定。

Answer 1

原來，子文件夾被搞砸了。 2與我有的子文件夾的數量相對應，我認爲我設置正確，但在「火車」內部只有1個子文件夾。

Answer 2

你的Y值是否也是一個熱點編碼？我只是想猜測爲什麼Y的形狀（？，2）。 如果你可以在你的訓練集中共享一些樣本標籤，那會很好。