Tensorflow】エラー報告の落とし穴--入力配列を形状(100,784)から形状(100)にブロードキャストできなかった。

tensorflowで自己コーディングマシンを実装すると、バッチの選択について以下のようなエラーが報告され続ける。

ValueError: 入力配列を形状 (100,784) から形状 (100) にブロードキャストすることができませんでした。

位置決めコードです。

batch_test_x = mnist.train.next_batch(batch_size)
You can see that there was a problem with selecting the dataset





When it comes to training, you need to select the right dataset for the batch, and there are two correct ways to do this.





Method 1: Use mnist.train.next_batch(batch_size)








When using the mnist.train.next_batch(batch_size) method to select a batch, be sure to note that the correct code is


batch_test_x, batch_test_y= mnist.train.next_batch(batch_size)




The dataset and label must be selected at the same time, and it is possible to throw only batch_test_x into the self-coding machine for training:.


cost,_ = sess.run([cost,optimizer],feed_dict={x:batch_test_x})




Method 2: Use a custom function to randomly select a range of datasets





The code is :


def get_random_block_from_data(data, batch_size):
    start_index = np.random.randint(0, len(data) - batch_size )
    return data[start_index:(start_index + batch_size)]

batch_test_x = get_random_block_from_data(x_train, batch_size)
c,_ = sess.run([cost,optimizer],feed_dict={x:batch_test_x})



At this point, it's time to throw batch_test_x into the self-coding machine for training









Method three.



batch = mnist.train.next_batch(50)
avg_acc = sess.run(accuracy, feed_dict={x: batch[0], y: batch[1], keep_prob: 1.0})



























The dataset and label must be selected at the same time, and it is possible to throw only batch_test_x into the self-coding machine for training:.

cost,_ = sess.run([cost,optimizer],feed_dict={x:batch_test_x})



Method 2: Use a custom function to randomly select a range of datasets


The code is :

def get_random_block_from_data(data, batch_size):
    start_index = np.random.randint(0, len(data) - batch_size )
    return data[start_index:(start_index + batch_size)]

batch_test_x = get_random_block_from_data(x_train, batch_size)
c,_ = sess.run([cost,optimizer],feed_dict={x:batch_test_x})




At this point, it's time to throw batch_test_x into the self-coding machine for training


Method three.



batch = mnist.train.next_batch(50)
avg_acc = sess.run(accuracy, feed_dict={x: batch[0], y: batch[1], keep_prob: 1.0})