Tensorflow 形状不匹配

Question

I am trying to write a code to create a neural network. It is supposed to read data from a particular csv file that contains 13 distinctive features for each individual inputs. Here is my code snippet:

n_inputs = 13
X = tf.placeholder(tf.float32, shape=(None, n_inputs), name="X")
y = tf.placeholder(tf.int64, shape=None, name="y")

def data_processor(n):
    id = pd.read_csv('./subset_numerical/'+patient_id[n])
    id_input = np.array(id['VALUE'].tolist())
    for s in sepsis_pat:
        if str(s) == str(patient_id[n].split('.')[0]):
            a = 1
    try:
        if a == 1:
            a = 0
            return [id_input, np.array([1, 0])]
    except:
        return [id_input, np.array([0, 1])]

My tf.Session() part looks like this:

with tf.Session() as sess:
init.run()
    for epoch in range(n_epochs):
        a = 0
        for iteration in range(300 // batch_size):
                X_batch, y_batch = data_processor(iteration)
                print((X_batch))
                sess.run(training_op, feed_dict={X: X_batch, y: y_batch})
                acc_train = accuracy.eval(feed_dict={X: X_batch, y: y_batch})
                print(epoch, "Train accuracy:", acc_train)
        save_path = saver.save(sess, "./my_model_final.ckpt")

The problem is : after execution, it shows the following error:

Can not feed value of shape (13,) for tensor 'X:0', which has shape (?,13) 

What is wrong with it?

Answer 1

Your X placeholder expects an input with shape=(None, n_inputs) and X_batch has the shape of n_inputs so the shapes don't match.

You can solve the problem by putting n_inputs into a list making its shape (1, n_inputs):

sess.run(training_op, feed_dict={X: [X_batch], y: y_batch})