Tensorflow分类回归“分配既需要张量的形状以匹配”

Question

我想下面的分类图表转换成回归，而不是让，而不是3个值返回只有一个值 -

baseFeatureSize = 5 
keep_prob = tf.placeholder(tf.float32) 
x = tf.placeholder(tf.float32, shape=[None, 64]) 
x_image = tf.reshape(x, [-1, 8, 8, 1]) 
W_conv1 = weight_variable([5, 5, 1, baseFeatureSize]) 
b_conv1 = bias_variable([baseFeatureSize]) 
h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1) 
W_conv2 = weight_variable([8, 8, baseFeatureSize, baseFeatureSize * 2]) 
b_conv2 = bias_variable([baseFeatureSize * 2]) 
h_conv2 = tf.nn.relu(conv2d(h_conv1, W_conv2) + b_conv2) 
W_fc1 = weight_variable([8 * 8 * baseFeatureSize * 2, baseFeatureSize * 4]) 
b_fc1 = bias_variable([baseFeatureSize * 4]) 
h_pool2_flat = tf.reshape(h_conv2, [-1, 8 * 8 * baseFeatureSize * 2]) 
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1) 
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob) 

W_fc3 = weight_variable([baseFeatureSize * 4, 3]) 
b_fc3 = bias_variable([3]) 
y_policy = tf.placeholder(tf.float32, shape=[None, 3]) 
y_policy_conv = tf.matmul(h_fc1, W_fc3) + b_fc3 
cross_entropy_policy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y_policy, logits=y_policy_conv)) 
train_step_policy = tf.train.AdamOptimizer(learning_rate = 0.01).minimize(cross_entropy_policy) 

对于它作为回归工作，我已经改变了完全连接的部分 - W_fc3，b_fc3，输出，交叉熵和train_step使得张量的形状尺寸为1而不是3，如下所示（图的其余部分保持不变） -

W_fc3 = weight_variable([baseFeatureSize * 4, 1]) 
b_fc3 = bias_variable([1]) 
y_policy = tf.placeholder(tf.float32, shape=[None, 1]) 
y_policy_conv = tf.nn.softmax(tf.matmul(h_fc1, W_fc3) + b_fc3) 
cross_entropy_policy = tf.reduce_mean(-tf.reduce_sum(y_policy * tf.log(y_policy_conv), reduction_indices=1)) 
train_step_policy = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy_policy) 

但它一直抛出以下错误 -

InvalidArgumentError（请参阅上面的回溯）：分配要求两个张量的形状要匹配。 lhs shape = [1] rhs shape = [3]

我无法在任何地方看到3。什么可能是错的？

Answer 1

以供将来参考：看到评论被阿基里斯：

对不起，我只是意识到发生了什么事。主管的logdir指向该模型的以前版本（分类版本）。我指出它是一个空的logdir，它工作。感谢您查看它。 - 跟腱