implementation_2_text_classification.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_path = \"data/sentiment_analysis/\"\n",
    "BATCH_SIZE = 48\n",
    "SEQ_LENGTH = 100\n",
    "EMB_DIM = 32 # embedding dimension\n",
    "HIDDEN_DIM = 64 # hidden state dimension of lstm cell\n",
    "emb_dict_file = data_path + \"sst_vocab.txt\"\n",
    "positive_file = data_path + \"sst_pos_sentences_id.txt\"\n",
    "negative_file = data_path + \"sst_neg_sentences_id.txt\"\n",
    "EPOCH_NUM = 1000\n",
    "graph_path = \"./graphs/implementation_2_graph\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Dataloader():\n",
    "    def __init__(self, batch_size, max_length = 100):\n",
    "        self.batch_size = batch_size\n",
    "        self.sentences = np.array([])\n",
    "        self.labels = np.array([])\n",
    "        self.max_length = max_length\n",
    "\n",
    "    def load_train_data(self, positive_file, negative_file):\n",
    "        # Load data\n",
    "        positive_examples = []\n",
    "        negative_examples = []\n",
    "        with open(positive_file)as fin:\n",
    "            for line in fin:\n",
    "                line = line.strip()\n",
    "                line = line.split()\n",
    "                parse_line = [int(x) for x in line]\n",
    "                parse_line.extend([0] * (self.max_length - len(parse_line)))\n",
    "                if len(parse_line) == self.max_length:\n",
    "                    positive_examples.append(parse_line)\n",
    "        with open(negative_file)as fin:\n",
    "            for line in fin:\n",
    "                line = line.strip()\n",
    "                line = line.split()\n",
    "                parse_line = [int(x) for x in line]\n",
    "                parse_line.extend([0] * (self.max_length - len(parse_line)))\n",
    "                if len(parse_line) == self.max_length:\n",
    "                    negative_examples.append(parse_line)\n",
    "        self.sentences = np.array(positive_examples + negative_examples)\n",
    "\n",
    "        # Generate labels\n",
    "        # positive_labels = [1 for _ in positive_examples]\n",
    "        # negative_labels = [0 for _ in negative_examples]\n",
    "        positive_labels = [[0, 1] for _ in positive_examples]\n",
    "        negative_labels = [[1, 0] for _ in negative_examples]\n",
    "        self.labels = np.concatenate([positive_labels, negative_labels], 0)\n",
    "        # self.labels = positive_labels + negative_labels\n",
    "\n",
    "        # Shuffle the data\n",
    "        shuffle_indices = np.random.permutation(np.arange(len(self.labels)))\n",
    "        self.sentences = self.sentences[shuffle_indices]\n",
    "        self.labels = self.labels[shuffle_indices]\n",
    "\n",
    "        # Split batches\n",
    "        self.num_batch = int(len(self.labels) / self.batch_size)\n",
    "        self.sentences = self.sentences[:self.num_batch * self.batch_size]\n",
    "        self.labels = self.labels[:self.num_batch * self.batch_size]\n",
    "        self.sentences_batches = np.split(self.sentences, self.num_batch, 0)\n",
    "        self.labels_batches = np.split(self.labels, self.num_batch, 0)\n",
    "\n",
    "        self.pointer = 0\n",
    "\n",
    "\n",
    "    def next_batch(self):\n",
    "        ret = self.sentences_batches[self.pointer], self.labels_batches[self.pointer]\n",
    "        self.pointer = (self.pointer + 1) % (self.num_batch - 5)\n",
    "        return ret\n",
    "\n",
    "    def test_batch(self):#Preserve part of dataset for testing\n",
    "        ret = self.sentences_batches[self.num_batch - 1], self.labels_batches[self.num_batch - 1]\n",
    "        return ret\n",
    "\n",
    "    def reset_pointer(self):\n",
    "        self.pointer = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Detection:\n",
    "    def __init__(self, sequence_length, batch_size, vocab_size, emb_dim, hidden_dim = 128, output_keep_prob=0.7):\n",
    "        self.num_emb = vocab_size  # vocab size\n",
    "        self.batch_size = batch_size  # batch size\n",
    "        self.emb_dim = emb_dim  # dimision of embedding\n",
    "        self.hidden_dim = hidden_dim  # hidden size\n",
    "        self.sequence_length = sequence_length  # sequence length\n",
    "        self.output_dim = 2\n",
    "        self.output_keep_prob = output_keep_prob #to prevent overfit\n",
    "        with tf.variable_scope(\"placeholder\"):\n",
    "            self.x = tf.placeholder(shape=[self.batch_size, self.sequence_length], dtype=tf.int32)\n",
    "            self.targets = tf.placeholder(shape=[self.batch_size, self.output_dim], dtype=tf.int64)\n",
    "        with tf.variable_scope(\"embedding\"):\n",
    "            self.g_embeddings = tf.Variable(tf.random_uniform([self.num_emb, self.emb_dim], -1.0, 1.0), name=\"W_text\")\n",
    "            self.inputs= tf.nn.embedding_lookup(self.g_embeddings, self.x)  # seq_length x batch_size x emb_dim\n",
    "        with tf.variable_scope(\"rnn\"):\n",
    "            cell_bw = tf.contrib.rnn.BasicLSTMCell(self.hidden_dim, state_is_tuple=False)  # single lstm unit\n",
    "            cell_bw = tf.contrib.rnn.DropoutWrapper(cell_bw, output_keep_prob=self.output_keep_prob)\n",
    "            cell_fw = tf.contrib.rnn.BasicLSTMCell(self.hidden_dim, state_is_tuple=False)  # single lstm unit\n",
    "            cell_fw = tf.contrib.rnn.DropoutWrapper(cell_fw, output_keep_prob=self.output_keep_prob)\n",
    "        with tf.variable_scope(\"output\"):\n",
    "            self.outputs, self.states = tf.nn.bidirectional_dynamic_rnn(cell_bw, cell_fw, self.inputs, dtype=tf.float32)\n",
    "            self.outputs = tf.reshape(self.outputs, shape=[-1, self.sequence_length, self.hidden_dim])\n",
    "            self.outputs = tf.transpose(self.outputs, perm=[1, 0, 2])  # [batch_size,seq_length]\n",
    "            self.outputs = tf.reduce_mean(self.outputs, 0)\n",
    "            self.outputs = self.outputs[:self.batch_size] + self.outputs[self.batch_size:]\n",
    "            self.logits = tf.layers.dense(self.outputs, self.output_dim, name=\"logits\")\n",
    "            self.prob = tf.nn.softmax(self.logits, name=\"softmax_output\")\n",
    "        with tf.variable_scope(\"train\"):\n",
    "            self.loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=self.targets, logits=self.logits))\n",
    "            tvars = tf.trainable_variables()\n",
    "            max_grad_norm = 5\n",
    "            # We clip the gradients to prevent explosion\n",
    "            grads, _ = tf.clip_by_global_norm(tf.gradients(self.loss, tvars), max_grad_norm)\n",
    "            gradients = list(zip(grads, tvars))\n",
    "            self.train_op = tf.train.AdamOptimizer(0.005).apply_gradients(gradients)\n",
    "        with tf.variable_scope(\"accuracy\"):\n",
    "            self.accuracy = tf.equal(tf.argmax(self.targets, axis=1), tf.argmax(self.prob, axis=1))\n",
    "            \n",
    "    def train(self, sess, x_batch, y_batch):\n",
    "        _, loss = sess.run([self.train_op, self.loss], feed_dict={self.x:x_batch, self.targets:y_batch})\n",
    "        return loss\n",
    "\n",
    "    def predict(self, sess, x_batch):\n",
    "        prob = sess.run([self.prob], feed_dict={self.x:x_batch})\n",
    "        return prob\n",
    "\n",
    "    def get_accuracy(self, sess, x_batch, y_batch):\n",
    "        accuracy = sess.run([self.accuracy], feed_dict={self.x: x_batch, self.targets: y_batch})\n",
    "        return (accuracy[0].tolist().count(True) / len(x_batch))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_emb_data(emb_dict_file):\n",
    "    word_dict = {}\n",
    "    word_list = []\n",
    "    item = 0\n",
    "    with open(emb_dict_file, 'r', encoding=\"utf-8\") as f:\n",
    "        lines = f.readlines()\n",
    "        for line in lines:\n",
    "            word = line.strip()\n",
    "            word_dict[word] = item\n",
    "            item += 1\n",
    "            word_list.append(word)\n",
    "    length = len(word_dict)\n",
    "    print(\"Load embedding success! Num: %d\" % length)\n",
    "    return word_dict, length, word_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Load embedding success! Num: 4734\n",
      "WARNING:tensorflow:From <ipython-input-4-c47a1050fae4>:17: BasicLSTMCell.__init__ (from tensorflow.python.ops.rnn_cell_impl) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "This class is deprecated, please use tf.nn.rnn_cell.LSTMCell, which supports all the feature this cell currently has. Please replace the existing code with tf.nn.rnn_cell.LSTMCell(name='basic_lstm_cell').\n",
      "WARNING:tensorflow:<tensorflow.python.ops.rnn_cell_impl.BasicLSTMCell object at 0x000001B26F713240>: Using a concatenated state is slower and will soon be deprecated.  Use state_is_tuple=True.\n",
      "WARNING:tensorflow:<tensorflow.python.ops.rnn_cell_impl.BasicLSTMCell object at 0x000001B272AC3710>: Using a concatenated state is slower and will soon be deprecated.  Use state_is_tuple=True.\n",
      "WARNING:tensorflow:From <ipython-input-4-c47a1050fae4>:30: softmax_cross_entropy_with_logits (from tensorflow.python.ops.nn_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "\n",
      "Future major versions of TensorFlow will allow gradients to flow\n",
      "into the labels input on backprop by default.\n",
      "\n",
      "See `tf.nn.softmax_cross_entropy_with_logits_v2`.\n",
      "\n"
     ]
    },
    {
     "ename": "InternalError",
     "evalue": "Blas GEMM launch failed : a.shape=(48, 96), b.shape=(96, 256), m=48, n=256, k=96\n\t [[node output/bidirectional_rnn/fw/fw/while/basic_lstm_cell/MatMul (defined at <ipython-input-4-c47a1050fae4>:22)  = MatMul[T=DT_FLOAT, transpose_a=false, transpose_b=false, _device=\"/job:localhost/replica:0/task:0/device:GPU:0\"](output/bidirectional_rnn/fw/fw/while/basic_lstm_cell/concat, output/bidirectional_rnn/fw/fw/while/basic_lstm_cell/MatMul/Enter)]]\n\t [[{{node train/Mean/_23}} = _Recv[client_terminated=false, recv_device=\"/job:localhost/replica:0/task:0/device:CPU:0\", send_device=\"/job:localhost/replica:0/task:0/device:GPU:0\", send_device_incarnation=1, tensor_name=\"edge_1108_train/Mean\", tensor_type=DT_FLOAT, _device=\"/job:localhost/replica:0/task:0/device:CPU:0\"]()]]\n\nCaused by op 'output/bidirectional_rnn/fw/fw/while/basic_lstm_cell/MatMul', defined at:\n  File \"C:\\ProgramData\\Anaconda3\\lib\\runpy.py\", line 193, in _run_module_as_main\n    \"__main__\", mod_spec)\n  File \"C:\\ProgramData\\Anaconda3\\lib\\runpy.py\", line 85, in _run_code\n    exec(code, run_globals)\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py\", line 16, in <module>\n    app.launch_new_instance()\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\traitlets\\config\\application.py\", line 658, in launch_instance\n    app.start()\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel\\kernelapp.py\", line 486, in start\n    self.io_loop.start()\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\tornado\\platform\\asyncio.py\", line 127, in start\n    self.asyncio_loop.run_forever()\n  File \"C:\\ProgramData\\Anaconda3\\lib\\asyncio\\base_events.py\", line 422, in run_forever\n    self._run_once()\n  File \"C:\\ProgramData\\Anaconda3\\lib\\asyncio\\base_events.py\", line 1432, in _run_once\n    handle._run()\n  File \"C:\\ProgramData\\Anaconda3\\lib\\asyncio\\events.py\", line 145, in _run\n    self._callback(*self._args)\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\tornado\\ioloop.py\", line 759, in _run_callback\n    ret = callback()\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\tornado\\stack_context.py\", line 276, in null_wrapper\n    return fn(*args, **kwargs)\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\zmq\\eventloop\\zmqstream.py\", line 536, in <lambda>\n    self.io_loop.add_callback(lambda : self._handle_events(self.socket, 0))\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\zmq\\eventloop\\zmqstream.py\", line 450, in _handle_events\n    self._handle_recv()\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\zmq\\eventloop\\zmqstream.py\", line 480, in _handle_recv\n    self._run_callback(callback, msg)\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\zmq\\eventloop\\zmqstream.py\", line 432, in _run_callback\n    callback(*args, **kwargs)\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\tornado\\stack_context.py\", line 276, in null_wrapper\n    return fn(*args, **kwargs)\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 283, in dispatcher\n    return self.dispatch_shell(stream, msg)\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 233, in dispatch_shell\n    handler(stream, idents, msg)\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 399, in execute_request\n    user_expressions, allow_stdin)\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel\\ipkernel.py\", line 208, in do_execute\n    res = shell.run_cell(code, store_history=store_history, silent=silent)\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel\\zmqshell.py\", line 537, in run_cell\n    return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2662, in run_cell\n    raw_cell, store_history, silent, shell_futures)\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2785, in _run_cell\n    interactivity=interactivity, compiler=compiler, result=result)\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2903, in run_ast_nodes\n    if self.run_code(code, result):\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2963, in run_code\n    exec(code_obj, self.user_global_ns, self.user_ns)\n  File \"<ipython-input-6-ae1613e45622>\", line 5, in <module>\n    detection = Detection(SEQ_LENGTH, BATCH_SIZE, vocab_size, EMB_DIM, HIDDEN_DIM)\n  File \"<ipython-input-4-c47a1050fae4>\", line 22, in __init__\n    self.outputs, self.states = tf.nn.bidirectional_dynamic_rnn(cell_bw, cell_fw, self.inputs, dtype=tf.float32)\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\ops\\rnn.py\", line 439, in bidirectional_dynamic_rnn\n    time_major=time_major, scope=fw_scope)\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\ops\\rnn.py\", line 664, in dynamic_rnn\n    dtype=dtype)\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\ops\\rnn.py\", line 872, in _dynamic_rnn_loop\n    swap_memory=swap_memory)\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\ops\\control_flow_ops.py\", line 3291, in while_loop\n    return_same_structure)\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\ops\\control_flow_ops.py\", line 3004, in BuildLoop\n    pred, body, original_loop_vars, loop_vars, shape_invariants)\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\ops\\control_flow_ops.py\", line 2939, in _BuildLoop\n    body_result = body(*packed_vars_for_body)\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\ops\\control_flow_ops.py\", line 3260, in <lambda>\n    body = lambda i, lv: (i + 1, orig_body(*lv))\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\ops\\rnn.py\", line 840, in _time_step\n    (output, new_state) = call_cell()\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\ops\\rnn.py\", line 826, in <lambda>\n    call_cell = lambda: cell(input_t, state)\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\ops\\rnn_cell_impl.py\", line 1282, in __call__\n    output, new_state = self._cell(inputs, state, scope=scope)\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\ops\\rnn_cell_impl.py\", line 370, in __call__\n    *args, **kwargs)\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\layers\\base.py\", line 374, in __call__\n    outputs = super(Layer, self).__call__(inputs, *args, **kwargs)\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\base_layer.py\", line 757, in __call__\n    outputs = self.call(inputs, *args, **kwargs)\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\ops\\rnn_cell_impl.py\", line 748, in call\n    array_ops.concat([inputs, h], 1), self._kernel)\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\ops\\math_ops.py\", line 2057, in matmul\n    a, b, transpose_a=transpose_a, transpose_b=transpose_b, name=name)\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\ops\\gen_math_ops.py\", line 4857, in mat_mul\n    name=name)\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\framework\\op_def_library.py\", line 787, in _apply_op_helper\n    op_def=op_def)\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\util\\deprecation.py\", line 488, in new_func\n    return func(*args, **kwargs)\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py\", line 3274, in create_op\n    op_def=op_def)\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py\", line 1770, in __init__\n    self._traceback = tf_stack.extract_stack()\n\nInternalError (see above for traceback): Blas GEMM launch failed : a.shape=(48, 96), b.shape=(96, 256), m=48, n=256, k=96\n\t [[node output/bidirectional_rnn/fw/fw/while/basic_lstm_cell/MatMul (defined at <ipython-input-4-c47a1050fae4>:22)  = MatMul[T=DT_FLOAT, transpose_a=false, transpose_b=false, _device=\"/job:localhost/replica:0/task:0/device:GPU:0\"](output/bidirectional_rnn/fw/fw/while/basic_lstm_cell/concat, output/bidirectional_rnn/fw/fw/while/basic_lstm_cell/MatMul/Enter)]]\n\t [[{{node train/Mean/_23}} = _Recv[client_terminated=false, recv_device=\"/job:localhost/replica:0/task:0/device:CPU:0\", send_device=\"/job:localhost/replica:0/task:0/device:GPU:0\", send_device_incarnation=1, tensor_name=\"edge_1108_train/Mean\", tensor_type=DT_FLOAT, _device=\"/job:localhost/replica:0/task:0/device:CPU:0\"]()]]\n",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mInternalError\u001b[0m                             Traceback (most recent call last)",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_do_call\u001b[1;34m(self, fn, *args)\u001b[0m\n\u001b[0;32m   1333\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1334\u001b[1;33m       \u001b[1;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1335\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_run_fn\u001b[1;34m(feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[0;32m   1318\u001b[0m       return self._call_tf_sessionrun(\n\u001b[1;32m-> 1319\u001b[1;33m           options, feed_dict, fetch_list, target_list, run_metadata)\n\u001b[0m\u001b[0;32m   1320\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_call_tf_sessionrun\u001b[1;34m(self, options, feed_dict, fetch_list, target_list, run_metadata)\u001b[0m\n\u001b[0;32m   1406\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moptions\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1407\u001b[1;33m         run_metadata)\n\u001b[0m\u001b[0;32m   1408\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mInternalError\u001b[0m: Blas GEMM launch failed : a.shape=(48, 96), b.shape=(96, 256), m=48, n=256, k=96\n\t [[{{node output/bidirectional_rnn/fw/fw/while/basic_lstm_cell/MatMul}} = MatMul[T=DT_FLOAT, transpose_a=false, transpose_b=false, _device=\"/job:localhost/replica:0/task:0/device:GPU:0\"](output/bidirectional_rnn/fw/fw/while/basic_lstm_cell/concat, output/bidirectional_rnn/fw/fw/while/basic_lstm_cell/MatMul/Enter)]]\n\t [[{{node train/Mean/_23}} = _Recv[client_terminated=false, recv_device=\"/job:localhost/replica:0/task:0/device:CPU:0\", send_device=\"/job:localhost/replica:0/task:0/device:GPU:0\", send_device_incarnation=1, tensor_name=\"edge_1108_train/Mean\", tensor_type=DT_FLOAT, _device=\"/job:localhost/replica:0/task:0/device:CPU:0\"]()]]",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mInternalError\u001b[0m                             Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-6-ae1613e45622>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     11\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mEPOCH_NUM\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     12\u001b[0m             \u001b[0mx_batch\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_batch\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdis_data_loader\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnext_batch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 13\u001b[1;33m             \u001b[0mloss\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdetection\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msess\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx_batch\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_batch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     14\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mi\u001b[0m \u001b[1;33m%\u001b[0m \u001b[1;36m20\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     15\u001b[0m                 \u001b[0maccuracy\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdetection\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_accuracy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msess\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtest_x_batch\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtest_y_batch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-4-c47a1050fae4>\u001b[0m in \u001b[0;36mtrain\u001b[1;34m(self, sess, x_batch, y_batch)\u001b[0m\n\u001b[0;32m     39\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     40\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mtrain\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msess\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx_batch\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_batch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 41\u001b[1;33m         \u001b[0m_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mloss\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msess\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtrain_op\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloss\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0mx_batch\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtargets\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0my_batch\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     42\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mloss\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     43\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36mrun\u001b[1;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[0;32m    927\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    928\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[1;32m--> 929\u001b[1;33m                          run_metadata_ptr)\n\u001b[0m\u001b[0;32m    930\u001b[0m       \u001b[1;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    931\u001b[0m         \u001b[0mproto_data\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_run\u001b[1;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[0;32m   1150\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mhandle\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mfeed_dict_tensor\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1151\u001b[0m       results = self._do_run(handle, final_targets, final_fetches,\n\u001b[1;32m-> 1152\u001b[1;33m                              feed_dict_tensor, options, run_metadata)\n\u001b[0m\u001b[0;32m   1153\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1154\u001b[0m       \u001b[0mresults\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_do_run\u001b[1;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001b[0m\n\u001b[0;32m   1326\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1327\u001b[0m       return self._do_call(_run_fn, feeds, fetches, targets, options,\n\u001b[1;32m-> 1328\u001b[1;33m                            run_metadata)\n\u001b[0m\u001b[0;32m   1329\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1330\u001b[0m       \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_do_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_prun_fn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfeeds\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfetches\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_do_call\u001b[1;34m(self, fn, *args)\u001b[0m\n\u001b[0;32m   1346\u001b[0m           \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1347\u001b[0m       \u001b[0mmessage\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0merror_interpolation\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minterpolate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmessage\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_graph\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1348\u001b[1;33m       \u001b[1;32mraise\u001b[0m \u001b[0mtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnode_def\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mop\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmessage\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1349\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1350\u001b[0m   \u001b[1;32mdef\u001b[0m \u001b[0m_extend_graph\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mInternalError\u001b[0m: Blas GEMM launch failed : a.shape=(48, 96), b.shape=(96, 256), m=48, n=256, k=96\n\t [[node output/bidirectional_rnn/fw/fw/while/basic_lstm_cell/MatMul (defined at <ipython-input-4-c47a1050fae4>:22)  = MatMul[T=DT_FLOAT, transpose_a=false, transpose_b=false, _device=\"/job:localhost/replica:0/task:0/device:GPU:0\"](output/bidirectional_rnn/fw/fw/while/basic_lstm_cell/concat, output/bidirectional_rnn/fw/fw/while/basic_lstm_cell/MatMul/Enter)]]\n\t [[{{node train/Mean/_23}} = _Recv[client_terminated=false, recv_device=\"/job:localhost/replica:0/task:0/device:CPU:0\", send_device=\"/job:localhost/replica:0/task:0/device:GPU:0\", send_device_incarnation=1, tensor_name=\"edge_1108_train/Mean\", tensor_type=DT_FLOAT, _device=\"/job:localhost/replica:0/task:0/device:CPU:0\"]()]]\n\nCaused by op 'output/bidirectional_rnn/fw/fw/while/basic_lstm_cell/MatMul', defined at:\n  File \"C:\\ProgramData\\Anaconda3\\lib\\runpy.py\", line 193, in _run_module_as_main\n    \"__main__\", mod_spec)\n  File \"C:\\ProgramData\\Anaconda3\\lib\\runpy.py\", line 85, in _run_code\n    exec(code, run_globals)\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py\", line 16, in <module>\n    app.launch_new_instance()\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\traitlets\\config\\application.py\", line 658, in launch_instance\n    app.start()\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel\\kernelapp.py\", line 486, in start\n    self.io_loop.start()\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\tornado\\platform\\asyncio.py\", line 127, in start\n    self.asyncio_loop.run_forever()\n  File \"C:\\ProgramData\\Anaconda3\\lib\\asyncio\\base_events.py\", line 422, in run_forever\n    self._run_once()\n  File \"C:\\ProgramData\\Anaconda3\\lib\\asyncio\\base_events.py\", line 1432, in _run_once\n    handle._run()\n  File \"C:\\ProgramData\\Anaconda3\\lib\\asyncio\\events.py\", line 145, in _run\n    self._callback(*self._args)\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\tornado\\ioloop.py\", line 759, in _run_callback\n    ret = callback()\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\tornado\\stack_context.py\", line 276, in null_wrapper\n    return fn(*args, **kwargs)\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\zmq\\eventloop\\zmqstream.py\", line 536, in <lambda>\n    self.io_loop.add_callback(lambda : self._handle_events(self.socket, 0))\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\zmq\\eventloop\\zmqstream.py\", line 450, in _handle_events\n    self._handle_recv()\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\zmq\\eventloop\\zmqstream.py\", line 480, in _handle_recv\n    self._run_callback(callback, msg)\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\zmq\\eventloop\\zmqstream.py\", line 432, in _run_callback\n    callback(*args, **kwargs)\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\tornado\\stack_context.py\", line 276, in null_wrapper\n    return fn(*args, **kwargs)\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 283, in dispatcher\n    return self.dispatch_shell(stream, msg)\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 233, in dispatch_shell\n    handler(stream, idents, msg)\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 399, in execute_request\n    user_expressions, allow_stdin)\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel\\ipkernel.py\", line 208, in do_execute\n    res = shell.run_cell(code, store_history=store_history, silent=silent)\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel\\zmqshell.py\", line 537, in run_cell\n    return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2662, in run_cell\n    raw_cell, store_history, silent, shell_futures)\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2785, in _run_cell\n    interactivity=interactivity, compiler=compiler, result=result)\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2903, in run_ast_nodes\n    if self.run_code(code, result):\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2963, in run_code\n    exec(code_obj, self.user_global_ns, self.user_ns)\n  File \"<ipython-input-6-ae1613e45622>\", line 5, in <module>\n    detection = Detection(SEQ_LENGTH, BATCH_SIZE, vocab_size, EMB_DIM, HIDDEN_DIM)\n  File \"<ipython-input-4-c47a1050fae4>\", line 22, in __init__\n    self.outputs, self.states = tf.nn.bidirectional_dynamic_rnn(cell_bw, cell_fw, self.inputs, dtype=tf.float32)\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\ops\\rnn.py\", line 439, in bidirectional_dynamic_rnn\n    time_major=time_major, scope=fw_scope)\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\ops\\rnn.py\", line 664, in dynamic_rnn\n    dtype=dtype)\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\ops\\rnn.py\", line 872, in _dynamic_rnn_loop\n    swap_memory=swap_memory)\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\ops\\control_flow_ops.py\", line 3291, in while_loop\n    return_same_structure)\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\ops\\control_flow_ops.py\", line 3004, in BuildLoop\n    pred, body, original_loop_vars, loop_vars, shape_invariants)\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\ops\\control_flow_ops.py\", line 2939, in _BuildLoop\n    body_result = body(*packed_vars_for_body)\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\ops\\control_flow_ops.py\", line 3260, in <lambda>\n    body = lambda i, lv: (i + 1, orig_body(*lv))\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\ops\\rnn.py\", line 840, in _time_step\n    (output, new_state) = call_cell()\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\ops\\rnn.py\", line 826, in <lambda>\n    call_cell = lambda: cell(input_t, state)\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\ops\\rnn_cell_impl.py\", line 1282, in __call__\n    output, new_state = self._cell(inputs, state, scope=scope)\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\ops\\rnn_cell_impl.py\", line 370, in __call__\n    *args, **kwargs)\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\layers\\base.py\", line 374, in __call__\n    outputs = super(Layer, self).__call__(inputs, *args, **kwargs)\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\base_layer.py\", line 757, in __call__\n    outputs = self.call(inputs, *args, **kwargs)\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\ops\\rnn_cell_impl.py\", line 748, in call\n    array_ops.concat([inputs, h], 1), self._kernel)\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\ops\\math_ops.py\", line 2057, in matmul\n    a, b, transpose_a=transpose_a, transpose_b=transpose_b, name=name)\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\ops\\gen_math_ops.py\", line 4857, in mat_mul\n    name=name)\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\framework\\op_def_library.py\", line 787, in _apply_op_helper\n    op_def=op_def)\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\util\\deprecation.py\", line 488, in new_func\n    return func(*args, **kwargs)\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py\", line 3274, in create_op\n    op_def=op_def)\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py\", line 1770, in __init__\n    self._traceback = tf_stack.extract_stack()\n\nInternalError (see above for traceback): Blas GEMM launch failed : a.shape=(48, 96), b.shape=(96, 256), m=48, n=256, k=96\n\t [[node output/bidirectional_rnn/fw/fw/while/basic_lstm_cell/MatMul (defined at <ipython-input-4-c47a1050fae4>:22)  = MatMul[T=DT_FLOAT, transpose_a=false, transpose_b=false, _device=\"/job:localhost/replica:0/task:0/device:GPU:0\"](output/bidirectional_rnn/fw/fw/while/basic_lstm_cell/concat, output/bidirectional_rnn/fw/fw/while/basic_lstm_cell/MatMul/Enter)]]\n\t [[{{node train/Mean/_23}} = _Recv[client_terminated=false, recv_device=\"/job:localhost/replica:0/task:0/device:CPU:0\", send_device=\"/job:localhost/replica:0/task:0/device:GPU:0\", send_device_incarnation=1, tensor_name=\"edge_1108_train/Mean\", tensor_type=DT_FLOAT, _device=\"/job:localhost/replica:0/task:0/device:CPU:0\"]()]]\n"
     ]
    }
   ],
   "source": [
    "if __name__ == \"__main__\":\n",
    "    vocab_dict, vocab_size, vocab_list = load_emb_data(emb_dict_file)\n",
    "    dis_data_loader = Dataloader(BATCH_SIZE, SEQ_LENGTH)\n",
    "    dis_data_loader.load_train_data(positive_file, negative_file)\n",
    "    detection = Detection(SEQ_LENGTH, BATCH_SIZE, vocab_size, EMB_DIM, HIDDEN_DIM)\n",
    "    test_x_batch, test_y_batch = dis_data_loader.test_batch()\n",
    "    with tf.Session() as sess:\n",
    "        sess.run(tf.global_variables_initializer())\n",
    "        writer = tf.summary.FileWriter(graph_path, sess.graph)\n",
    "        writer.close()\n",
    "        for i in range(EPOCH_NUM):\n",
    "            x_batch, y_batch = dis_data_loader.next_batch()\n",
    "            loss = detection.train(sess, x_batch, y_batch)\n",
    "            if (i % 20 == 0):\n",
    "                accuracy = detection.get_accuracy(sess, test_x_batch, test_y_batch)\n",
    "                print(\"%d, loss:%f, accuracy:%f\" % (i, loss, accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}