{"action":"created","issue":{"url":"https://api.github.com/repos/tensorflow/tensorflow/issues/3897","repository_url":"https://api.github.com/repos/tensorflow/tensorflow","labels_url":"https://api.github.com/repos/tensorflow/tensorflow/issues/3897/labels{/name}","comments_url":"https://api.github.com/repos/tensorflow/tensorflow/issues/3897/comments","events_url":"https://api.github.com/repos/tensorflow/tensorflow/issues/3897/events","html_url":"https://github.com/tensorflow/tensorflow/issues/3897","id":171856978,"number":3897,"title":"Tensorflow gradients are always zero!","user":{"login":"inderpreetsganda","id":10248819,"avatar_url":"https://avatars.githubusercontent.com/u/10248819?v=3","gravatar_id":"","url":"https://api.github.com/users/inderpreetsganda","html_url":"https://github.com/inderpreetsganda","followers_url":"https://api.github.com/users/inderpreetsganda/followers","following_url":"https://api.github.com/users/inderpreetsganda/following{/other_user}","gists_url":"https://api.github.com/users/inderpreetsganda/gists{/gist_id}","starred_url":"https://api.github.com/users/inderpreetsganda/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/inderpreetsganda/subscriptions","organizations_url":"https://api.github.com/users/inderpreetsganda/orgs","repos_url":"https://api.github.com/users/inderpreetsganda/repos","events_url":"https://api.github.com/users/inderpreetsganda/events{/privacy}","received_events_url":"https://api.github.com/users/inderpreetsganda/received_events","type":"User","site_admin":false},"labels":[],"state":"closed","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":3,"created_at":"2016-08-18T09:28:11Z","updated_at":"2016-08-20T06:19:18Z","closed_at":"2016-08-20T06:19:18Z","body":"Tensorflow gradients are always zero with respect to conv layers that are after first conv layer. I've tried different ways to check that but gradients are always zero! Here is the small reproducible code that can be run to check that.\r\n```\r\nfrom __future__ import absolute_import\r\nfrom __future__ import division\r\nfrom __future__ import print_function\r\n\r\nimport tensorflow as tf\r\nimport numpy as np\r\nimport math\r\nimport os\r\nimport random\r\nimport tflearn\r\nbatch_size = 100\r\nstart = 0\r\nend = batch_size\r\nlearning_rate = 0.000001\r\nnum_classes = 4\r\ntime_steps = 4\r\nembedding = 2\r\nstep = 1\r\n_units = 500\r\nnum_of_filters = 1000\r\n\r\ntrain_set_x = [[[1,2],[3,4],[5,6],[7,8]],[[1,2],[3,4],[5,6],[7,8]]]\r\ntrain_set_y = [0,1]\r\n\r\nX = tf.placeholder(tf.float32, [None,time_steps,embedding])\r\nY = tf.placeholder(tf.int32, [None])\r\n\r\n\r\nx = tf.expand_dims(X,3)\r\n       \r\nfilter_shape = [1, embedding, 1, num_of_filters]\r\nconv_weights = tf.get_variable(\"conv_weights1\" , filter_shape, tf.float32, tf.contrib.layers.xavier_initializer())\r\nconv_biases = tf.Variable(tf.constant(0.1, shape=[num_of_filters]))\r\nconv = tf.nn.conv2d(x, conv_weights, strides=[1,1,1,1], padding = \"VALID\")\r\nnormalize = conv + conv_biases\r\ntf_normalize = tflearn.layers.normalization.batch_normalization(normalize)\r\nrelu = tf.nn.elu(tf_normalize)\r\npooling = tf.reduce_max(relu, reduction_indices = 3, keep_dims = True)\r\noutputs_fed_lstm = pooling\r\n   \r\nfilter_shape2 = [1, 1, 1, num_of_filters]\r\nconv_weights2 = tf.get_variable(\"conv_weights2\" , filter_shape2, tf.float32, tf.contrib.layers.xavier_initializer())\r\nconv_biases2 = tf.Variable(tf.constant(0.1, shape=[num_of_filters]))\r\nconv2 = tf.nn.conv2d(outputs_fed_lstm, conv_weights2, strides=[1,1,1,1], padding = \"VALID\")\r\nnormalize2 = conv2 + conv_biases2\r\ntf_normalize2 = tflearn.layers.normalization.batch_normalization(normalize2)\r\nrelu2 = tf.nn.elu(tf_normalize2)\r\npooling2 = tf.reduce_max(relu2, reduction_indices = 3, keep_dims = True)\r\noutputs_fed_lstm2 = pooling2\r\n         \r\nx = tf.squeeze(outputs_fed_lstm2, [2])     \r\nx = tf.transpose(x, [1, 0, 2])\r\nx = tf.reshape(x, [-1, 1])\r\nx = tf.split(0, time_steps, x)\r\n      \r\nlstm = tf.nn.rnn_cell.LSTMCell(num_units = _units)\r\n      \r\n# multi_lstm = tf.nn.rnn_cell.MultiRNNCell([lstm] * lstm_layers, state_is_tuple = True)\r\n     \r\noutputs , state = tf.nn.rnn(lstm,x, dtype = tf.float32)     \r\n     \r\nweights = tf.Variable(tf.random_normal([_units,num_classes]))\r\nbiases  = tf.Variable(tf.random_normal([num_classes]))\r\n\r\nlogits = tf.matmul(outputs[-1], weights) + biases\r\n    \r\n\r\n\r\nc_loss = tf.nn.sparse_softmax_cross_entropy_with_logits(logits,Y)\r\nloss = tf.reduce_mean(c_loss)\r\n    \r\n\r\nglobal_step = tf.Variable(0, name=\"global_step\", trainable=False)\r\n# decayed_learning_rate = tf.train.exponential_decay(learning_rate,0,10000,0.9)\r\noptimizer= tf.train.AdamOptimizer(learning_rate)\r\nminimize_loss = optimizer.minimize(loss, global_step=global_step)   \r\ngrads_and_vars = optimizer.compute_gradients(loss,[conv_weights2]) \r\ncorrect_predict = tf.nn.in_top_k(logits, Y, 1)\r\naccuracy = tf.reduce_mean(tf.cast(correct_predict, tf.float32))\r\n    \r\n    \r\ninit = tf.initialize_all_variables()\r\n\r\nwith tf.Session() as sess:\r\n     sess.run(init)\r\n     for i in range(1):\r\n         for j in range(1):\r\n             x = train_set_x\r\n             y = train_set_y\r\n             sess.run(minimize_loss,feed_dict={X : x, Y : y})\r\n             step += 1  \r\n             gr_print = sess.run([grad for grad, _ in grads_and_vars], feed_dict={X : x, Y : y})\r\n             print (gr_print)\r\n             cost = sess.run(loss,feed_dict = {X: x,Y: y})\r\n             accu = sess.run(accuracy,feed_dict = {X: x, Y: y})\r\n             print (\"Loss after one Epoch(Training) = \" + \"{:.6f}\".format(cost) + \", Training Accuracy= \" + \"{:.5f}\".format(accu))\r\n```\r\nAnd here is the output\r\n\r\n```\r\n[array([[[[ 0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\r\n           0.        ,  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dtype=float32)]\r\n```\r\n"},"comment":{"url":"https://api.github.com/repos/tensorflow/tensorflow/issues/comments/241182464","html_url":"https://github.com/tensorflow/tensorflow/issues/3897#issuecomment-241182464","issue_url":"https://api.github.com/repos/tensorflow/tensorflow/issues/3897","id":241182464,"user":{"login":"inderpreetsganda","id":10248819,"avatar_url":"https://avatars.githubusercontent.com/u/10248819?v=3","gravatar_id":"","url":"https://api.github.com/users/inderpreetsganda","html_url":"https://github.com/inderpreetsganda","followers_url":"https://api.github.com/users/inderpreetsganda/followers","following_url":"https://api.github.com/users/inderpreetsganda/following{/other_user}","gists_url":"https://api.github.com/users/inderpreetsganda/gists{/gist_id}","starred_url":"https://api.github.com/users/inderpreetsganda/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/inderpreetsganda/subscriptions","organizations_url":"https://api.github.com/users/inderpreetsganda/orgs","repos_url":"https://api.github.com/users/inderpreetsganda/repos","events_url":"https://api.github.com/users/inderpreetsganda/events{/privacy}","received_events_url":"https://api.github.com/users/inderpreetsganda/received_events","type":"User","site_admin":false},"created_at":"2016-08-20T06:19:18Z","updated_at":"2016-08-20T06:19:18Z","body":"This isn't an issue, there was problem with my architecture. Thanks @yaroslavvb and @suiyuan2009 "}}
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