我刚开始学习TensorFlow，正在尝试训练以下两层网络。看起来它不起作用，因为交叉熵并未随着迭代次数的增加而减少。我认为我搞错了隐藏层与输出层的连接。如果你能看出问题所在，请帮助我，
import tensorflow as tffrom scipy.io import loadmatimport numpy as npimport sysx = loadmat('../mnist_data/ex4data1.mat')X = x['X']# one hot conversiony_temp = x['y']y_temp = np.reshape(y_temp, (len(y_temp),))y = np.zeros((len(y_temp),10))y[np.arange(len(y_temp)), y_temp-1] = 1.input_size = 400hidden1_size = 25output_size = 10num_iters = 50reg_alpha = 0.05x = tf.placeholder(tf.float32, [None, input_size], name='data')W1 = tf.Variable(tf.zeros([hidden1_size, input_size], tf.float32, name='weights_1st_layer'))b1 = tf.Variable(tf.zeros([hidden1_size], tf.float32), name='bias_layer_1')W2 = tf.Variable(tf.zeros([output_size, hidden1_size], tf.float32, name='weights_2nd_layer'))b2 = tf.Variable(tf.zeros([output_size], tf.float32), name='bias_layer_2')hidden_op = tf.nn.relu(tf.add(tf.matmul(x, W1, transpose_b=True), b1))output_op = tf.matmul(hidden_op, W2, transpose_b=True) + b2pred = tf.nn.softmax(output_op) y_ = tf.placeholder(tf.float32, [None, 10], name='actual_labels')cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits( labels=y_, logits=output_op))train_step = tf.train.GradientDescentOptimizer(reg_alpha).minimize(cross_entropy)sess = tf.InteractiveSession()tf.global_variables_initializer().run()for _ in range(50): print ('training..', _) print (sess.run([train_step, cross_entropy], feed_dict={x : X, y_ : y}))corr_pred = tf.equal(tf.argmax(pred, axis=1), tf.argmax(y_, axis=1))acc = tf.reduce_mean(tf.cast(corr_pred, tf.float32))print (sess.run(acc, feed_dict={x:X, y_:y}))sess.close()回答：
尝试将你的权重初始化为随机值，而不是零。
所以不要使用：
W1 = tf.Variable(tf.zeros([hidden1_size, input_size], tf.float32, name='weights_1st_layer'))W2 = tf.Variable(tf.zeros([output_size, hidden1_size], tf.float32, name='weights_2nd_layer'))而是使用：
W1 = tf.Variable(tf.truncated_normal([hidden1_size, input_size], tf.float32, name='weights_1st_layer'), stddev=0.1))W2 = tf.Variable(tf.truncated_normal([output_size, hidden1_size], tf.float32, name='weights_2nd_layer'), stddev=0.1))查看这里有一个很好的总结，解释了为什么将所有权重初始化为零会阻止网络进行训练。
