我收到了这个错误消息：ValueError: 无法为形状为(1, 2)的值馈送到形状为'(?, 1, 2)’的Tensor u’Placeholder:0′
我的训练和测试数据有2个特征
[[10, 10],[1,2],[3,2]...]我的目标数据如下所示：
[[0, 1], [1, 0], [1, 0]...]这是我的代码：
training_data = np.vstack(training_data)training_target = np.vstack(training_target)test_data = np.vstack(test_data)test_target = np.vstack(test_target)learning_rate = 0.001n_input = 2 n_steps = 1 n_hidden = 128 n_classes = 2 # tf Graph inputx = tf.placeholder("float", [None, n_steps, n_input])y = tf.placeholder("float", [None, n_classes])# Define weightsweights = { 'out': tf.Variable(tf.random_normal([n_hidden, n_classes]))}biases = { 'out': tf.Variable(tf.random_normal([n_classes]))}def RNN(x, weights, biases): x = tf.unstack(x, n_steps, 1) # Define a lstm cell with tensorflow lstm_cell = rnn.BasicLSTMCell(n_hidden, forget_bias=1.0) # Get lstm cell output outputs, states = rnn.static_rnn(lstm_cell, x, dtype=tf.float32) # Linear activation, using rnn inner loop last output return tf.matmul(outputs[-1], weights['out']) + biases['out']pred = RNN(x, weights, biases)# Define loss and optimizercost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y))optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)# Evaluate modelcorrect_pred = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))# Initializing the variablesinit = tf.global_variables_initializer()# Launch the graphwith tf.Session() as sess: sess.run(init) step = 1 for i in range(len(training_data)): batch_x = training_data[i] batch_y = training_target[i] print(batch_x) print(batch_y) batch_x = tf.reshape(batch_x, [1, 2]).eval() print(batch_x) sess.run(optimizer, feed_dict={x: batch_x, y: batch_y}) acc = sess.run(accuracy, feed_dict={x: batch_x, y: batch_y}) loss = sess.run(cost, feed_dict={x: batch_x, y: batch_y}) print("Iter " + str(step) + ", Minibatch Loss= " + "{:.6f}".format(loss) + ", Training Accuracy= " + "{:.5f}".format(acc)) print("Optimization Finished!") print("Testing Accuracy:", sess.run(accuracy, feed_dict={x: test_data, y: test_target}))我需要帮助来重塑数据，我还没有实现下一个批次的函数，只是想让这个工作起来。
没有包括我加载CSV文件的部分，…等等。
任何关于代码的评论都很好，谢谢你。
回答：
你试图馈送的数组不符合占位符的维度。例如，对于batch_x，你试图将[1, 2]馈送到[?, 1, 2]，对于batch_y，你试图将[2]馈送到[?, 2]。如果你批次大小为1，你应该将[1, 1, 2]馈送到[?, 1, 2]，将[1, 2]馈送到[?, 2]分别对应batch_x和batch_y。
batch_size = 1with tf.Session() as sess: sess.run(init) step = 1 for i in range(len(training_data)): batch_x = training_data[i] batch_y = training_target[i] batch_x = np.reshape(batch_x, [batch_size, 1, 2]) batch_y = np.reshape(batch_y, [batch_size, 2]) [_, acc, loss] = sess.run([optimizer, accuracy, cost], feed_dict={x: batch_x, y: batch_y}) print("Iter " + str(step) + ", Minibatch Loss= " + "{:.6f}".format(loss) + ", Training Accuracy= " + "{:.5f}".format(acc))