{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"c:\\users\\sa_li\\anaconda3\\envs\\rnn-tf-ker\\lib\\site-packages\\h5py\\__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
" from ._conv import register_converters as _register_converters\n"
]
}
],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib as mpl\n",
"import random\n",
"import math\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import tensorflow as tf\n",
"from tensorflow.python.framework import ops"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"#import data as array\n",
"# 8 hits with x,y,z\n",
"\n",
"testset = pd.read_pickle('matched_8hittracks.pkl')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(46896, 24)\n",
"[-20.411108 -9.417887 4.7599998]\n",
"[-27.813803 -6.944843 4.7599998]\n",
"[-66.736946 22.9032 4.3599997]\n",
"[-74.0961 35.649506 4.04 ]\n",
"[78.324196 26.359665 -3.7200012]\n",
"[69.040436 14.306461 -4.04 ]\n",
"[26.880571 -9.817033 -4.84 ]\n",
"[ 19.68401 -11.173258 -5. ]\n",
"[ -2.2485821 23.380732 -6.04 -6.489999 28.598572\n",
" -5.6400003 -21.724771 67.052704 -3.2400002 -22.225971\n",
" 79.267685 -2.6000004 82.22602 3.0700002 7.24\n",
" 70.390724 0.19000006 7.5599995 28.802656 3.9014618\n",
" 6.04 21.421392 6.978845 5.64 ]\n"
]
}
],
"source": [
"#Check testset with arbitrary particle\n",
"\n",
"tset = np.array(testset)\n",
"tset = tset.astype('float32')\n",
"print(tset.shape)\n",
"for i in range(8):\n",
" print(tset[1,3*i:(3*i+3)])\n",
"print(tset[0,:])"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"### Reshape original array into the shape (particlenumber, timesteps, input = coordinates)###\n",
"\n",
"def reshapor(arr_orig):\n",
" timesteps = int(arr_orig.shape[1]/3)\n",
" number_examples = int(arr_orig.shape[0])\n",
" arr = np.zeros((number_examples, timesteps, 3))\n",
" \n",
" for i in range(number_examples):\n",
" for t in range(timesteps):\n",
" arr[i,t,0:3] = arr_orig[i,3*t:3*t+3]\n",
" \n",
" return arr"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"### create the training set and the test set###\n",
"\n",
"def create_random_sets(dataset, train_to_total_ratio):\n",
" num_examples = dataset.shape[0]\n",
" train_set_size = np.int(num_examples*train_to_total_ratio)\n",
" test_set_size = num_examples - train_set_size\n",
" random_indices = random.sample(range(num_examples), train_set_size)\n",
" train_set = np.zeros((train_set_size, dataset.shape[1]))\n",
" test_set = np.zeros((test_set_size, dataset.shape[1]))\n",
" \n",
" trc=0\n",
" tec=0\n",
" \n",
" for i in range(num_examples):\n",
" if i in random_indices:\n",
" train_set[trc,:] += tset[i,:]\n",
" trc += 1\n",
" else:\n",
" test_set[tec,:] += tset[i,:]\n",
" tec +=1\n",
" \n",
" train_set = reshapor(train_set)\n",
" test_set = reshapor(test_set)\n",
" \n",
" return train_set, test_set\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(469, 8, 3) (46427, 8, 3) (46896, 8, 3)\n",
"[[ 8.08750057 -20.96217346 17.71999931]\n",
" [ 11.96380711 -26.65756416 18.60000038]\n",
" [ 23.27025223 -66.63859558 23.55999947]\n",
" [ 21.99198914 -79.3210907 24.84000015]\n",
" [-80.79417419 15.53796387 56.84000015]\n",
" [-68.08280182 17.88038635 57.88000107]\n",
" [-26.11420822 12.17565346 59.63999939]\n",
" [-20.3575325 9.54722977 59.72000122]]\n"
]
}
],
"source": [
"train_set, test_set = create_random_sets(tset, 0.99)\n",
"print(test_set.shape, train_set.shape, reshapor(tset).shape)\n",
"print(test_set[0,:,:])"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"### create target and input arrays input of shape (num_examples, 8 timesteps, n_inputs)###\n",
"\n",
"def target_and_input(data_set):\n",
" \n",
" num_ex = data_set.shape[0]\n",
" inputt = np.zeros((num_ex, 4, 12))\n",
" target = np.zeros((num_ex, 4, 3))\n",
" \n",
" \n",
" for i in range(4):\n",
" target[:,i,:] = data_set[:,4+i,:]\n",
" for f in range(4):\n",
" inputt[:,i,3*f:3*f+3] = data_set[:,i+f,:]\n",
" \n",
" \n",
" \n",
" \n",
" return inputt, target\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[ -2.24858212 23.38073158 -6.03999996 -6.48999882 28.59857178\n",
" -5.64000034 -21.7247715 67.05270386 -3.24000025 -22.22597122\n",
" 79.26768494 -2.60000038]\n",
" [ -6.48999882 28.59857178 -5.64000034 -21.7247715 67.05270386\n",
" -3.24000025 -22.22597122 79.26768494 -2.60000038 82.22602081\n",
" 3.07000017 7.23999977]\n",
" [-21.7247715 67.05270386 -3.24000025 -22.22597122 79.26768494\n",
" -2.60000038 82.22602081 3.07000017 7.23999977 70.39072418\n",
" 0.19000006 7.55999947]\n",
" [-22.22597122 79.26768494 -2.60000038 82.22602081 3.07000017\n",
" 7.23999977 70.39072418 0.19000006 7.55999947 28.80265617\n",
" 3.90146184 6.03999996]]\n",
"[[82.22602081 3.07000017 7.23999977]\n",
" [70.39072418 0.19000006 7.55999947]\n",
" [28.80265617 3.90146184 6.03999996]\n",
" [21.42139244 6.97884512 5.63999987]]\n"
]
}
],
"source": [
"inputt_train, target_train = target_and_input(train_set)\n",
"inputt_test, target_test = target_and_input(test_set)\n",
"print(inputt_train[0,:,:])\n",
"print(target_train[0,:,:])"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"###create random mini_batches###\n",
"\n",
"\n",
"def unison_shuffled_copies(a, b):\n",
" assert a.shape[0] == b.shape[0]\n",
" p = np.random.permutation(a.shape[0])\n",
" return a[p,:,:], b[p,:,:]\n",
"\n",
"def random_mini_batches(inputt, target, minibatch_size = 500):\n",
" \n",
" num_examples = inputt.shape[0]\n",
" \n",
" \n",
" #Number of complete batches\n",
" \n",
" number_of_batches = int(num_examples/minibatch_size)\n",
" minibatches = []\n",
" \n",
" #shuffle particles\n",
" _i, _t = unison_shuffled_copies(inputt, target)\n",
" print(_t.shape)\n",
" \n",
" \n",
" for i in range(number_of_batches):\n",
" \n",
" minibatch_train = _i[minibatch_size*i:minibatch_size*(i+1), :, :]\n",
" \n",
" minibatch_true = _t[minibatch_size*i:minibatch_size*(i+1), :, :]\n",
" \n",
" minibatches.append((minibatch_train, minibatch_true))\n",
" \n",
" \n",
" minibatches.append((_i[number_of_batches*minibatch_size:, :, :], _t[number_of_batches*minibatch_size:, :, :]))\n",
" \n",
" \n",
" return minibatches\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(46427, 4, 3)\n",
"93\n",
"(46427, 7, 3)\n",
"[[ 21.33720207 -7.18209839 27.63999939]\n",
" [ 26.04004478 -12.40390682 27.95999908]\n",
" [ 49.56869888 -49.9788208 29.87999916]\n",
" [ 53.79240036 -62.2730217 30.27999878]\n",
" [-79.5124588 21.15354919 51.55999756]\n",
" [-66.75765228 22.82592583 51.95999908]\n",
" [-24.64919281 17.44109154 51.79999924]] [[ 26.04004478 -12.40390682 27.95999908]\n",
" [ 49.56869888 -49.9788208 29.87999916]\n",
" [ 53.79240036 -62.2730217 30.27999878]\n",
" [-79.5124588 21.15354919 51.55999756]\n",
" [-66.75765228 22.82592583 51.95999908]\n",
" [-24.64919281 17.44109154 51.79999924]\n",
" [-18.15327644 14.86877632 51.79999924]]\n"
]
}
],
"source": [
"minibatches = random_mini_batches(inputt_train, target_train)\n",
"\n",
"\n",
"testinputt, testtarget = minibatches[int(inputt_train.shape[0]/500)]\n",
"\n",
"print(len(minibatches))\n",
"\n",
"minibatches = random_mini_batches(train_set[:,:-1,:], train_set[:,1:,:])\n",
"train, target = minibatches[0]\n",
"test_input, test_target = test_set[:,:-1,:], test_set[:,1:,:]\n",
"print(train[0,:,:], target[0,:,:])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"class RNNPlacePrediction():\n",
" \n",
" \n",
" def __init__(self, time_steps, future_steps, ninputs, ncells, num_output, cell_type=\"basic_rnn\"):\n",
" \n",
" self.nsteps = time_steps\n",
" self.future_steps = future_steps\n",
" self.ninputs = ninputs\n",
" self.ncells = ncells\n",
" self.num_output = num_output\n",
" \n",
" #### The input is of shape (nbatches, time_steps, ninputs)\n",
" #### ninputs is the dimentionality (number of features) of the time series\n",
" self.X = tf.placeholder(dtype=tf.float32, shape=(None, time_steps, ninputs))\n",
" self.Y = tf.placeholder(dtype=tf.float32, shape=(None, time_steps, ninputs))\n",
" \n",
" \n",
" if cell_type==\"basic_rnn\":\n",
" self.cell = tf.contrib.rnn.BasicRNNCell(num_units=ncells, activation=tf.nn.relu)\n",
" \n",
" elif cell_type==\"lstm\":\n",
" self.cell = tf.contrib.rnn.BasicLSTMCell(num_units=ncells, activation=tf.nn.relu)\n",
" \n",
" elif cell_type==\"GRU\":\n",
" self.cell = tf.contrib.rnn.GRUCell(num_units=ncells, activation=tf.nn.relu)\n",
" \n",
" else:\n",
" print(\"Wrong rnn cell type: \", cell_type)\n",
" assert(False)\n",
" \n",
" \n",
" #### I now define the output\n",
" self.RNNCell = tf.contrib.rnn.OutputProjectionWrapper(self.cell, output_size= num_output)\n",
" \n",
" \n",
" \n",
" \n",
" \n",
" self.sess = tf.Session()\n",
" \n",
" def set_cost_and_functions(self, LR=0.001):\n",
" #### I define here the function that unrolls the RNN cell\n",
" self.output, self.state = tf.nn.dynamic_rnn(self.RNNCell, self.X, dtype=tf.float32)\n",
" #### I define the cost function as the square error\n",
" self.cost = tf.reduce_mean(tf.losses.mean_squared_error(self.Y, self.output)) \n",
" \n",
" #### the rest proceed as usual\n",
" self.train = tf.train.AdamOptimizer(LR).minimize(self.cost)\n",
" #### Variable initializer\n",
" self.init = tf.global_variables_initializer()\n",
" self.saver = tf.train.Saver()\n",
" self.sess.run(self.init)\n",
" \n",
" \n",
" \n",
" def fit(self, minibatches, epochs, print_step):\n",
" \n",
" loss_list = []\n",
" \n",
" for iep in range(epochs):\n",
" loss = 0\n",
" for batch in range(len(minibatches)):\n",
" #### Here I train the RNNcell\n",
" #### The x is the time serie, the y is shifted by 1 time step\n",
" train, target = minibatches[batch]\n",
" self.sess.run(self.train, feed_dict={self.X:train, self.Y:target})\n",
" \n",
" \n",
" loss += self.sess.run(self.cost, feed_dict={self.X:train, self.Y:target})\n",
" \n",
" if iep%print_step==0:\n",
" print(\"Epoch number \",iep)\n",
" print(\"Cost: \",loss)\n",
" \n",
" loss_list.append(loss)\n",
" print(loss)\n",
" \n",
" \n",
" def save(self, filename=\"./rnn_model_GRU_30/rnn_basic\"):\n",
" self.saver.save(self.sess, filename)\n",
" \n",
" \n",
" def load(self, filename=\"./rnn_model_GRU_30/rnn_basic\"):\n",
" self.saver.restore(self.sess, filename)\n",
" \n",
" \n",
" def predict(self, x):\n",
" return self.sess.run(self.output, feed_dict={self.X:x})\n",
" \n",
" "
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"tf.reset_default_graph()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"timesteps = 7\n",
"future_steps = 1\n",
"ninputs = 3\n",
"ncells = 1\n",
"num_output = 3"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"WARNING:tensorflow:From c:\\users\\sa_li\\anaconda3\\envs\\rnn-tf-ker\\lib\\site-packages\\tensorflow\\contrib\\learn\\python\\learn\\datasets\\base.py:198: retry (from tensorflow.contrib.learn.python.learn.datasets.base) is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"Use the retry module or similar alternatives.\n"
]
}
],
"source": [
"rnn = RNNPlacePrediction(time_steps=timesteps, future_steps=future_steps, ninputs=ninputs, \n",
" ncells=ncells, num_output=num_output, cell_type=\"basic_rnn\")"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"rnn.set_cost_and_functions()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"ename": "InternalError",
"evalue": "Blas GEMV launch failed: m=4, n=500\n\t [[Node: rnn/while/rnn/output_projection_wrapper/basic_rnn_cell/MatMul = MatMul[T=DT_FLOAT, transpose_a=false, transpose_b=false, _device=\"/job:localhost/replica:0/task:0/device:GPU:0\"](rnn/while/rnn/output_projection_wrapper/basic_rnn_cell/concat, rnn/while/rnn/output_projection_wrapper/basic_rnn_cell/MatMul/Enter)]]\n\nCaused by op 'rnn/while/rnn/output_projection_wrapper/basic_rnn_cell/MatMul', defined at:\n File \"c:\\users\\sa_li\\anaconda3\\envs\\rnn-tf-ker\\lib\\runpy.py\", line 193, in _run_module_as_main\n \"__main__\", mod_spec)\n File \"c:\\users\\sa_li\\anaconda3\\envs\\rnn-tf-ker\\lib\\runpy.py\", line 85, in _run_code\n exec(code, run_globals)\n File \"c:\\users\\sa_li\\anaconda3\\envs\\rnn-tf-ker\\lib\\site-packages\\ipykernel_launcher.py\", line 16, in <module>\n app.launch_new_instance()\n File \"c:\\users\\sa_li\\anaconda3\\envs\\rnn-tf-ker\\lib\\site-packages\\traitlets\\config\\application.py\", line 658, in launch_instance\n app.start()\n File \"c:\\users\\sa_li\\anaconda3\\envs\\rnn-tf-ker\\lib\\site-packages\\ipykernel\\kernelapp.py\", line 486, in start\n self.io_loop.start()\n File \"c:\\users\\sa_li\\anaconda3\\envs\\rnn-tf-ker\\lib\\site-packages\\tornado\\platform\\asyncio.py\", line 112, in start\n self.asyncio_loop.run_forever()\n File \"c:\\users\\sa_li\\anaconda3\\envs\\rnn-tf-ker\\lib\\asyncio\\base_events.py\", line 422, in run_forever\n self._run_once()\n File \"c:\\users\\sa_li\\anaconda3\\envs\\rnn-tf-ker\\lib\\asyncio\\base_events.py\", line 1432, in _run_once\n handle._run()\n File \"c:\\users\\sa_li\\anaconda3\\envs\\rnn-tf-ker\\lib\\asyncio\\events.py\", line 145, in _run\n self._callback(*self._args)\n File \"c:\\users\\sa_li\\anaconda3\\envs\\rnn-tf-ker\\lib\\site-packages\\tornado\\ioloop.py\", line 760, in _run_callback\n ret = callback()\n File \"c:\\users\\sa_li\\anaconda3\\envs\\rnn-tf-ker\\lib\\site-packages\\tornado\\stack_context.py\", line 276, in null_wrapper\n return fn(*args, **kwargs)\n File \"c:\\users\\sa_li\\anaconda3\\envs\\rnn-tf-ker\\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:\\users\\sa_li\\anaconda3\\envs\\rnn-tf-ker\\lib\\site-packages\\zmq\\eventloop\\zmqstream.py\", line 450, in _handle_events\n self._handle_recv()\n File \"c:\\users\\sa_li\\anaconda3\\envs\\rnn-tf-ker\\lib\\site-packages\\zmq\\eventloop\\zmqstream.py\", line 480, in _handle_recv\n self._run_callback(callback, msg)\n File \"c:\\users\\sa_li\\anaconda3\\envs\\rnn-tf-ker\\lib\\site-packages\\zmq\\eventloop\\zmqstream.py\", line 432, in _run_callback\n callback(*args, **kwargs)\n File \"c:\\users\\sa_li\\anaconda3\\envs\\rnn-tf-ker\\lib\\site-packages\\tornado\\stack_context.py\", line 276, in null_wrapper\n return fn(*args, **kwargs)\n File \"c:\\users\\sa_li\\anaconda3\\envs\\rnn-tf-ker\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 283, in dispatcher\n return self.dispatch_shell(stream, msg)\n File \"c:\\users\\sa_li\\anaconda3\\envs\\rnn-tf-ker\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 233, in dispatch_shell\n handler(stream, idents, msg)\n File \"c:\\users\\sa_li\\anaconda3\\envs\\rnn-tf-ker\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 399, in execute_request\n user_expressions, allow_stdin)\n File \"c:\\users\\sa_li\\anaconda3\\envs\\rnn-tf-ker\\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:\\users\\sa_li\\anaconda3\\envs\\rnn-tf-ker\\lib\\site-packages\\ipykernel\\zmqshell.py\", line 537, in run_cell\n return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)\n File \"c:\\users\\sa_li\\anaconda3\\envs\\rnn-tf-ker\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2662, in run_cell\n raw_cell, store_history, silent, shell_futures)\n File \"c:\\users\\sa_li\\anaconda3\\envs\\rnn-tf-ker\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2785, in _run_cell\n interactivity=interactivity, compiler=compiler, result=result)\n File \"c:\\users\\sa_li\\anaconda3\\envs\\rnn-tf-ker\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2909, in run_ast_nodes\n if self.run_code(code, result):\n File \"c:\\users\\sa_li\\anaconda3\\envs\\rnn-tf-ker\\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-15-1675544dad11>\", line 1, in <module>\n rnn.set_cost_and_functions()\n File \"<ipython-input-11-ea8285714686>\", line 43, in set_cost_and_functions\n self.output, self.state = tf.nn.dynamic_rnn(self.RNNCell, self.X, dtype=tf.float32)\n File \"c:\\users\\sa_li\\anaconda3\\envs\\rnn-tf-ker\\lib\\site-packages\\tensorflow\\python\\ops\\rnn.py\", line 635, in dynamic_rnn\n dtype=dtype)\n File \"c:\\users\\sa_li\\anaconda3\\envs\\rnn-tf-ker\\lib\\site-packages\\tensorflow\\python\\ops\\rnn.py\", line 832, in _dynamic_rnn_loop\n swap_memory=swap_memory)\n File \"c:\\users\\sa_li\\anaconda3\\envs\\rnn-tf-ker\\lib\\site-packages\\tensorflow\\python\\ops\\control_flow_ops.py\", line 3202, in while_loop\n result = loop_context.BuildLoop(cond, body, loop_vars, shape_invariants)\n File \"c:\\users\\sa_li\\anaconda3\\envs\\rnn-tf-ker\\lib\\site-packages\\tensorflow\\python\\ops\\control_flow_ops.py\", line 2940, in BuildLoop\n pred, body, original_loop_vars, loop_vars, shape_invariants)\n File \"c:\\users\\sa_li\\anaconda3\\envs\\rnn-tf-ker\\lib\\site-packages\\tensorflow\\python\\ops\\control_flow_ops.py\", line 2877, in _BuildLoop\n body_result = body(*packed_vars_for_body)\n File \"c:\\users\\sa_li\\anaconda3\\envs\\rnn-tf-ker\\lib\\site-packages\\tensorflow\\python\\ops\\control_flow_ops.py\", line 3178, in <lambda>\n body = lambda i, lv: (i + 1, orig_body(*lv))\n File \"c:\\users\\sa_li\\anaconda3\\envs\\rnn-tf-ker\\lib\\site-packages\\tensorflow\\python\\ops\\rnn.py\", line 803, in _time_step\n (output, new_state) = call_cell()\n File \"c:\\users\\sa_li\\anaconda3\\envs\\rnn-tf-ker\\lib\\site-packages\\tensorflow\\python\\ops\\rnn.py\", line 789, in <lambda>\n call_cell = lambda: cell(input_t, state)\n File \"c:\\users\\sa_li\\anaconda3\\envs\\rnn-tf-ker\\lib\\site-packages\\tensorflow\\python\\ops\\rnn_cell_impl.py\", line 191, in __call__\n return super(RNNCell, self).__call__(inputs, state)\n File \"c:\\users\\sa_li\\anaconda3\\envs\\rnn-tf-ker\\lib\\site-packages\\tensorflow\\python\\layers\\base.py\", line 714, in __call__\n outputs = self.call(inputs, *args, **kwargs)\n File \"c:\\users\\sa_li\\anaconda3\\envs\\rnn-tf-ker\\lib\\site-packages\\tensorflow\\contrib\\rnn\\python\\ops\\core_rnn_cell.py\", line 382, in call\n output, res_state = self._cell(inputs, state)\n File \"c:\\users\\sa_li\\anaconda3\\envs\\rnn-tf-ker\\lib\\site-packages\\tensorflow\\python\\ops\\rnn_cell_impl.py\", line 298, in __call__\n *args, **kwargs)\n File \"c:\\users\\sa_li\\anaconda3\\envs\\rnn-tf-ker\\lib\\site-packages\\tensorflow\\python\\layers\\base.py\", line 714, in __call__\n outputs = self.call(inputs, *args, **kwargs)\n File \"c:\\users\\sa_li\\anaconda3\\envs\\rnn-tf-ker\\lib\\site-packages\\tensorflow\\python\\ops\\rnn_cell_impl.py\", line 353, in call\n array_ops.concat([inputs, state], 1), self._kernel)\n File \"c:\\users\\sa_li\\anaconda3\\envs\\rnn-tf-ker\\lib\\site-packages\\tensorflow\\python\\ops\\math_ops.py\", line 2108, in matmul\n a, b, transpose_a=transpose_a, transpose_b=transpose_b, name=name)\n File \"c:\\users\\sa_li\\anaconda3\\envs\\rnn-tf-ker\\lib\\site-packages\\tensorflow\\python\\ops\\gen_math_ops.py\", line 4492, in mat_mul\n name=name)\n File \"c:\\users\\sa_li\\anaconda3\\envs\\rnn-tf-ker\\lib\\site-packages\\tensorflow\\python\\framework\\op_def_library.py\", line 787, in _apply_op_helper\n op_def=op_def)\n File \"c:\\users\\sa_li\\anaconda3\\envs\\rnn-tf-ker\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py\", line 3290, in create_op\n op_def=op_def)\n File \"c:\\users\\sa_li\\anaconda3\\envs\\rnn-tf-ker\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py\", line 1654, in __init__\n self._traceback = self._graph._extract_stack() # pylint: disable=protected-access\n\nInternalError (see above for traceback): Blas GEMV launch failed: m=4, n=500\n\t [[Node: rnn/while/rnn/output_projection_wrapper/basic_rnn_cell/MatMul = MatMul[T=DT_FLOAT, transpose_a=false, transpose_b=false, _device=\"/job:localhost/replica:0/task:0/device:GPU:0\"](rnn/while/rnn/output_projection_wrapper/basic_rnn_cell/concat, rnn/while/rnn/output_projection_wrapper/basic_rnn_cell/MatMul/Enter)]]\n",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mInternalError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32mc:\\users\\sa_li\\anaconda3\\envs\\rnn-tf-ker\\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 1326\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1327\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 1328\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:\\users\\sa_li\\anaconda3\\envs\\rnn-tf-ker\\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 1311\u001b[0m return self._call_tf_sessionrun(\n\u001b[1;32m-> 1312\u001b[1;33m options, feed_dict, fetch_list, target_list, run_metadata)\n\u001b[0m\u001b[0;32m 1313\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mc:\\users\\sa_li\\anaconda3\\envs\\rnn-tf-ker\\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 1419\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-> 1420\u001b[1;33m status, run_metadata)\n\u001b[0m\u001b[0;32m 1421\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mc:\\users\\sa_li\\anaconda3\\envs\\rnn-tf-ker\\lib\\site-packages\\tensorflow\\python\\framework\\errors_impl.py\u001b[0m in \u001b[0;36m__exit__\u001b[1;34m(self, type_arg, value_arg, traceback_arg)\u001b[0m\n\u001b[0;32m 515\u001b[0m \u001b[0mcompat\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mc_api\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTF_Message\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstatus\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstatus\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;32m--> 516\u001b[1;33m c_api.TF_GetCode(self.status.status))\n\u001b[0m\u001b[0;32m 517\u001b[0m \u001b[1;31m# Delete the underlying status object from memory otherwise it stays alive\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mInternalError\u001b[0m: Blas GEMV launch failed: m=4, n=500\n\t [[Node: rnn/while/rnn/output_projection_wrapper/basic_rnn_cell/MatMul = MatMul[T=DT_FLOAT, transpose_a=false, transpose_b=false, _device=\"/job:localhost/replica:0/task:0/device:GPU:0\"](rnn/while/rnn/output_projection_wrapper/basic_rnn_cell/concat, rnn/while/rnn/output_projection_wrapper/basic_rnn_cell/MatMul/Enter)]]",
"\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-16-bd64c2feca52>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mrnn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mminibatches\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m5000\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mprint_step\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m500\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;32m<ipython-input-11-ea8285714686>\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, minibatches, epochs, print_step)\u001b[0m\n\u001b[0;32m 64\u001b[0m \u001b[1;31m#### The x is the time serie, the y is shifted by 1 time step\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 65\u001b[0m \u001b[0mtrain\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtarget\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mminibatches\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mbatch\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 66\u001b[1;33m \u001b[0mself\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[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtrain\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[0mtrain\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mY\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0mtarget\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 67\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 68\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mc:\\users\\sa_li\\anaconda3\\envs\\rnn-tf-ker\\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 903\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 904\u001b[0m result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[1;32m--> 905\u001b[1;33m run_metadata_ptr)\n\u001b[0m\u001b[0;32m 906\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 907\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:\\users\\sa_li\\anaconda3\\envs\\rnn-tf-ker\\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 1138\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 1139\u001b[0m results = self._do_run(handle, final_targets, final_fetches,\n\u001b[1;32m-> 1140\u001b[1;33m feed_dict_tensor, options, run_metadata)\n\u001b[0m\u001b[0;32m 1141\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1142\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:\\users\\sa_li\\anaconda3\\envs\\rnn-tf-ker\\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 1319\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 1320\u001b[0m return self._do_call(_run_fn, feeds, fetches, targets, options,\n\u001b[1;32m-> 1321\u001b[1;33m run_metadata)\n\u001b[0m\u001b[0;32m 1322\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1323\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:\\users\\sa_li\\anaconda3\\envs\\rnn-tf-ker\\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 1338\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1339\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1340\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 1341\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1342\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 GEMV launch failed: m=4, n=500\n\t [[Node: rnn/while/rnn/output_projection_wrapper/basic_rnn_cell/MatMul = MatMul[T=DT_FLOAT, transpose_a=false, transpose_b=false, _device=\"/job:localhost/replica:0/task:0/device:GPU:0\"](rnn/while/rnn/output_projection_wrapper/basic_rnn_cell/concat, rnn/while/rnn/output_projection_wrapper/basic_rnn_cell/MatMul/Enter)]]\n\nCaused by op 'rnn/while/rnn/output_projection_wrapper/basic_rnn_cell/MatMul', defined at:\n File \"c:\\users\\sa_li\\anaconda3\\envs\\rnn-tf-ker\\lib\\runpy.py\", line 193, in _run_module_as_main\n \"__main__\", mod_spec)\n File \"c:\\users\\sa_li\\anaconda3\\envs\\rnn-tf-ker\\lib\\runpy.py\", line 85, in _run_code\n exec(code, run_globals)\n File \"c:\\users\\sa_li\\anaconda3\\envs\\rnn-tf-ker\\lib\\site-packages\\ipykernel_launcher.py\", line 16, in <module>\n app.launch_new_instance()\n File \"c:\\users\\sa_li\\anaconda3\\envs\\rnn-tf-ker\\lib\\site-packages\\traitlets\\config\\application.py\", line 658, in launch_instance\n app.start()\n File \"c:\\users\\sa_li\\anaconda3\\envs\\rnn-tf-ker\\lib\\site-packages\\ipykernel\\kernelapp.py\", line 486, in start\n self.io_loop.start()\n File \"c:\\users\\sa_li\\anaconda3\\envs\\rnn-tf-ker\\lib\\site-packages\\tornado\\platform\\asyncio.py\", line 112, in start\n self.asyncio_loop.run_forever()\n File \"c:\\users\\sa_li\\anaconda3\\envs\\rnn-tf-ker\\lib\\asyncio\\base_events.py\", line 422, in run_forever\n self._run_once()\n File \"c:\\users\\sa_li\\anaconda3\\envs\\rnn-tf-ker\\lib\\asyncio\\base_events.py\", line 1432, in _run_once\n handle._run()\n File \"c:\\users\\sa_li\\anaconda3\\envs\\rnn-tf-ker\\lib\\asyncio\\events.py\", line 145, in _run\n self._callback(*self._args)\n File \"c:\\users\\sa_li\\anaconda3\\envs\\rnn-tf-ker\\lib\\site-packages\\tornado\\ioloop.py\", line 760, in _run_callback\n ret = callback()\n File \"c:\\users\\sa_li\\anaconda3\\envs\\rnn-tf-ker\\lib\\site-packages\\tornado\\stack_context.py\", line 276, in null_wrapper\n return fn(*args, **kwargs)\n File \"c:\\users\\sa_li\\anaconda3\\envs\\rnn-tf-ker\\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:\\users\\sa_li\\anaconda3\\envs\\rnn-tf-ker\\lib\\site-packages\\zmq\\eventloop\\zmqstream.py\", line 450, in _handle_events\n self._handle_recv()\n File \"c:\\users\\sa_li\\anaconda3\\envs\\rnn-tf-ker\\lib\\site-packages\\zmq\\eventloop\\zmqstream.py\", line 480, in _handle_recv\n self._run_callback(callback, msg)\n File \"c:\\users\\sa_li\\anaconda3\\envs\\rnn-tf-ker\\lib\\site-packages\\zmq\\eventloop\\zmqstream.py\", line 432, in _run_callback\n callback(*args, **kwargs)\n File \"c:\\users\\sa_li\\anaconda3\\envs\\rnn-tf-ker\\lib\\site-packages\\tornado\\stack_context.py\", line 276, in null_wrapper\n return fn(*args, **kwargs)\n File \"c:\\users\\sa_li\\anaconda3\\envs\\rnn-tf-ker\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 283, in dispatcher\n return self.dispatch_shell(stream, msg)\n File \"c:\\users\\sa_li\\anaconda3\\envs\\rnn-tf-ker\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 233, in dispatch_shell\n handler(stream, idents, msg)\n File \"c:\\users\\sa_li\\anaconda3\\envs\\rnn-tf-ker\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 399, in execute_request\n user_expressions, allow_stdin)\n File \"c:\\users\\sa_li\\anaconda3\\envs\\rnn-tf-ker\\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:\\users\\sa_li\\anaconda3\\envs\\rnn-tf-ker\\lib\\site-packages\\ipykernel\\zmqshell.py\", line 537, in run_cell\n return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)\n File \"c:\\users\\sa_li\\anaconda3\\envs\\rnn-tf-ker\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2662, in run_cell\n raw_cell, store_history, silent, shell_futures)\n File \"c:\\users\\sa_li\\anaconda3\\envs\\rnn-tf-ker\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2785, in _run_cell\n interactivity=interactivity, compiler=compiler, result=result)\n File \"c:\\users\\sa_li\\anaconda3\\envs\\rnn-tf-ker\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2909, in run_ast_nodes\n if self.run_code(code, result):\n File \"c:\\users\\sa_li\\anaconda3\\envs\\rnn-tf-ker\\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-15-1675544dad11>\", line 1, in <module>\n rnn.set_cost_and_functions()\n File \"<ipython-input-11-ea8285714686>\", line 43, in set_cost_and_functions\n self.output, self.state = tf.nn.dynamic_rnn(self.RNNCell, self.X, dtype=tf.float32)\n File \"c:\\users\\sa_li\\anaconda3\\envs\\rnn-tf-ker\\lib\\site-packages\\tensorflow\\python\\ops\\rnn.py\", line 635, in dynamic_rnn\n dtype=dtype)\n File \"c:\\users\\sa_li\\anaconda3\\envs\\rnn-tf-ker\\lib\\site-packages\\tensorflow\\python\\ops\\rnn.py\", line 832, in _dynamic_rnn_loop\n swap_memory=swap_memory)\n File \"c:\\users\\sa_li\\anaconda3\\envs\\rnn-tf-ker\\lib\\site-packages\\tensorflow\\python\\ops\\control_flow_ops.py\", line 3202, in while_loop\n result = loop_context.BuildLoop(cond, body, loop_vars, shape_invariants)\n File \"c:\\users\\sa_li\\anaconda3\\envs\\rnn-tf-ker\\lib\\site-packages\\tensorflow\\python\\ops\\control_flow_ops.py\", line 2940, in BuildLoop\n pred, body, original_loop_vars, loop_vars, shape_invariants)\n File \"c:\\users\\sa_li\\anaconda3\\envs\\rnn-tf-ker\\lib\\site-packages\\tensorflow\\python\\ops\\control_flow_ops.py\", line 2877, in _BuildLoop\n body_result = body(*packed_vars_for_body)\n File \"c:\\users\\sa_li\\anaconda3\\envs\\rnn-tf-ker\\lib\\site-packages\\tensorflow\\python\\ops\\control_flow_ops.py\", line 3178, in <lambda>\n body = lambda i, lv: (i + 1, orig_body(*lv))\n File \"c:\\users\\sa_li\\anaconda3\\envs\\rnn-tf-ker\\lib\\site-packages\\tensorflow\\python\\ops\\rnn.py\", line 803, in _time_step\n (output, new_state) = call_cell()\n File \"c:\\users\\sa_li\\anaconda3\\envs\\rnn-tf-ker\\lib\\site-packages\\tensorflow\\python\\ops\\rnn.py\", line 789, in <lambda>\n call_cell = lambda: cell(input_t, state)\n File \"c:\\users\\sa_li\\anaconda3\\envs\\rnn-tf-ker\\lib\\site-packages\\tensorflow\\python\\ops\\rnn_cell_impl.py\", line 191, in __call__\n return super(RNNCell, self).__call__(inputs, state)\n File \"c:\\users\\sa_li\\anaconda3\\envs\\rnn-tf-ker\\lib\\site-packages\\tensorflow\\python\\layers\\base.py\", line 714, in __call__\n outputs = self.call(inputs, *args, **kwargs)\n File \"c:\\users\\sa_li\\anaconda3\\envs\\rnn-tf-ker\\lib\\site-packages\\tensorflow\\contrib\\rnn\\python\\ops\\core_rnn_cell.py\", line 382, in call\n output, res_state = self._cell(inputs, state)\n File \"c:\\users\\sa_li\\anaconda3\\envs\\rnn-tf-ker\\lib\\site-packages\\tensorflow\\python\\ops\\rnn_cell_impl.py\", line 298, in __call__\n *args, **kwargs)\n File \"c:\\users\\sa_li\\anaconda3\\envs\\rnn-tf-ker\\lib\\site-packages\\tensorflow\\python\\layers\\base.py\", line 714, in __call__\n outputs = self.call(inputs, *args, **kwargs)\n File \"c:\\users\\sa_li\\anaconda3\\envs\\rnn-tf-ker\\lib\\site-packages\\tensorflow\\python\\ops\\rnn_cell_impl.py\", line 353, in call\n array_ops.concat([inputs, state], 1), self._kernel)\n File \"c:\\users\\sa_li\\anaconda3\\envs\\rnn-tf-ker\\lib\\site-packages\\tensorflow\\python\\ops\\math_ops.py\", line 2108, in matmul\n a, b, transpose_a=transpose_a, transpose_b=transpose_b, name=name)\n File \"c:\\users\\sa_li\\anaconda3\\envs\\rnn-tf-ker\\lib\\site-packages\\tensorflow\\python\\ops\\gen_math_ops.py\", line 4492, in mat_mul\n name=name)\n File \"c:\\users\\sa_li\\anaconda3\\envs\\rnn-tf-ker\\lib\\site-packages\\tensorflow\\python\\framework\\op_def_library.py\", line 787, in _apply_op_helper\n op_def=op_def)\n File \"c:\\users\\sa_li\\anaconda3\\envs\\rnn-tf-ker\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py\", line 3290, in create_op\n op_def=op_def)\n File \"c:\\users\\sa_li\\anaconda3\\envs\\rnn-tf-ker\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py\", line 1654, in __init__\n self._traceback = self._graph._extract_stack() # pylint: disable=protected-access\n\nInternalError (see above for traceback): Blas GEMV launch failed: m=4, n=500\n\t [[Node: rnn/while/rnn/output_projection_wrapper/basic_rnn_cell/MatMul = MatMul[T=DT_FLOAT, transpose_a=false, transpose_b=false, _device=\"/job:localhost/replica:0/task:0/device:GPU:0\"](rnn/while/rnn/output_projection_wrapper/basic_rnn_cell/concat, rnn/while/rnn/output_projection_wrapper/basic_rnn_cell/MatMul/Enter)]]\n"
]
}
],
"source": [
"rnn.fit(minibatches, epochs=5000, print_step=500)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"rnn.save()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"###rnn.load()###"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"###test_input.shape###"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#test_pred = rnn.predict(test_input)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#print(test_pred[5,:,:]-test_target[5,:,:])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#rnn.sess.run(rnn.cost, feed_dict={rnn.X:test_input, rnn.Y:test_target})"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": []
},
{
"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
}
