テンソルフローのトレーニングで重みとコストが変わらない

Question

私は多人数で、マルチ入力とマルチ出力の問題を解決するためにテンソルを使用しようとしました。しかし、トレーニングの過程で、ネットワークの重量とコストは変わりません。ここにいくつかのメインコードがありますが、どんな提案も感謝しています！

learning_rate = 0.01 
training_epoch = 2000 
batch_size = 100 
display_step = 1 

# place holder for graph input 
x = tf.placeholder("float64", [None, 14]) 
y = tf.placeholder("float64", [None, 8]) 

# model weights 
w_1 = tf.Variable(tf.zeros([14, 11], dtype = tf.float64)) 
w_2 = tf.Variable(tf.zeros([11, 8], dtype = tf.float64)) 

# construct a model 
h_in = tf.matmul(x, w_1) 
h_out = tf.nn.relu(h_in) 
o_in = tf.matmul(h_out, w_2) 
o_out = tf.nn.relu(o_in) 

# cost: mean square error 
cost = tf.reduce_sum(tf.pow((o_out - y), 2)) 

# optimizer 
optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost) 

# initializer 
init = tf.global_variables_initializer() 

# launch the graph 
with tf.Session() as sess: 
    sess.run(init) 

    for epoch in range(training_epoch): 
     pos = 0; 
     # loop over all batches 
     if pos < train_input_array.shape[0]: 
      # get the next batch 
      batch_i = [] 
      batch_o = [] 
      for i in range(pos, pos + batch_size): 
       batch_i.append(train_input_array[i].tolist()) 
       batch_o.append(train_output_array[i].tolist()) 
      np.array(batch_i) 
      np.array(batch_o) 
      pos += batch_size; 
     sess.run(optimizer, feed_dict = {x: batch_i, y: batch_o}) 
     print sess.run(w_2[0]) 

     if (epoch + 1) % display_step == 0: 
      c = sess.run(cost, feed_dict = {x: batch_i, y: batch_o}) 
      print("Epoch: ", "%04d" % (epoch + 1), "cost: ", "{:.9f}".format(c)) 

Answer 1

は、私はあなたが私がreduce_meanするためにそれを変更し

# reduce sum doesn't work 
cost = tf.reduce_sum(tf.pow((o_out - y), 2)) 
# you need to use mean 
cost = tf.reduce_mean(tf.pow((o_out - y), 2))