Complete Linear Regression Program.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Complete Linear Regression Program"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import tensorflow as tf# Model parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Basic Regression Program"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "W: [-0.9999969] b: [ 0.99999082] loss: 5.69997e-11\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import tensorflow as tf# Model parameters\n",
    "W = tf.Variable([.3], dtype=tf.float32)\n",
    "b = tf.Variable([-.3], dtype=tf.float32)\n",
    "# Model input and output\n",
    "x = tf.placeholder(tf.float32)\n",
    "linear_model = W * x + b\n",
    "y = tf.placeholder(tf.float32)\n",
    "# loss\n",
    "loss = tf.reduce_sum(tf.square(linear_model - y)) # sum of the squares\n",
    "# optimizer\n",
    "optimizer = tf.train.GradientDescentOptimizer(0.01)\n",
    "train = optimizer.minimize(loss)\n",
    "# training data\n",
    "x_train = [1,2,3,4]\n",
    "y_train = [0,-1,-2,-3]\n",
    "# training loop\n",
    "init = tf.global_variables_initializer()\n",
    "sess = tf.Session()\n",
    "sess.run(init) # reset values to wrong\n",
    "for i in range(1000):\n",
    "  sess.run(train, {x:x_train, y:y_train})# evaluate training accuracy\n",
    "curr_W, curr_b, curr_loss = sess.run([W, b, loss], {x:x_train, y:y_train})\n",
    "print(\"W: %s b: %s loss: %s\"%(curr_W, curr_b, curr_loss))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Shortened using tf.contrib.learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Using default config.\n",
      "WARNING:tensorflow:Using temporary folder as model directory: C:\\Users\\ichis\\AppData\\Local\\Temp\\tmpm5iwmvvk\n",
      "INFO:tensorflow:Using config: {'_keep_checkpoint_max': 5, '_is_chief': True, '_keep_checkpoint_every_n_hours': 10000, '_save_checkpoints_steps': None, '_master': '', '_tf_config': gpu_options {\n",
      "  per_process_gpu_memory_fraction: 1\n",
      "}\n",
      ", '_task_id': 0, '_num_ps_replicas': 0, '_environment': 'local', '_save_summary_steps': 100, '_save_checkpoints_secs': 600, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x00000133EBA513C8>, '_session_config': None, '_model_dir': 'C:\\\\Users\\\\ichis\\\\AppData\\\\Local\\\\Temp\\\\tmpm5iwmvvk', '_evaluation_master': '', '_num_worker_replicas': 0, '_task_type': None, '_tf_random_seed': None}\n",
      "WARNING:tensorflow:From c:\\users\\ichis\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\contrib\\learn\\python\\learn\\estimators\\head.py:625: scalar_summary (from tensorflow.python.ops.logging_ops) is deprecated and will be removed after 2016-11-30.\n",
      "Instructions for updating:\n",
      "Please switch to tf.summary.scalar. Note that tf.summary.scalar uses the node name instead of the tag. This means that TensorFlow will automatically de-duplicate summary names based on the scope they are created in. Also, passing a tensor or list of tags to a scalar summary op is no longer supported.\n",
      "INFO:tensorflow:Create CheckpointSaverHook.\n"
     ]
    },
    {
     "ename": "InternalError",
     "evalue": "Blas GEMV launch failed:  m=1, n=4\n\t [[Node: linear/linear/x/matmul = MatMul[T=DT_FLOAT, transpose_a=false, transpose_b=false, _device=\"/job:localhost/replica:0/task:0/gpu:0\"](linear/linear/x/ExpandDims, linear/x/weight)]]\n\t [[Node: linear/regression_head/train_op/Ftrl/update/_92 = _Recv[client_terminated=false, recv_device=\"/job:localhost/replica:0/task:0/cpu:0\", send_device=\"/job:localhost/replica:0/task:0/gpu:0\", send_device_incarnation=1, tensor_name=\"edge_80_linear/regression_head/train_op/Ftrl/update\", tensor_type=DT_FLOAT, _device=\"/job:localhost/replica:0/task:0/cpu:0\"]()]]\n\nCaused by op 'linear/linear/x/matmul', defined at:\n  File \"c:\\users\\ichis\\anaconda3\\envs\\tensorflow\\lib\\runpy.py\", line 193, in _run_module_as_main\n    \"__main__\", mod_spec)\n  File \"c:\\users\\ichis\\anaconda3\\envs\\tensorflow\\lib\\runpy.py\", line 85, in _run_code\n    exec(code, run_globals)\n  File \"c:\\users\\ichis\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\ipykernel_launcher.py\", line 16, in <module>\n    app.launch_new_instance()\n  File \"c:\\users\\ichis\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\traitlets\\config\\application.py\", line 658, in launch_instance\n    app.start()\n  File \"c:\\users\\ichis\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\ipykernel\\kernelapp.py\", line 477, in start\n    ioloop.IOLoop.instance().start()\n  File \"c:\\users\\ichis\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\zmq\\eventloop\\ioloop.py\", line 177, in start\n    super(ZMQIOLoop, self).start()\n  File \"c:\\users\\ichis\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\tornado\\ioloop.py\", line 888, in start\n    handler_func(fd_obj, events)\n  File \"c:\\users\\ichis\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\tornado\\stack_context.py\", line 277, in null_wrapper\n    return fn(*args, **kwargs)\n  File \"c:\\users\\ichis\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\zmq\\eventloop\\zmqstream.py\", line 440, in _handle_events\n    self._handle_recv()\n  File \"c:\\users\\ichis\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\zmq\\eventloop\\zmqstream.py\", line 472, in _handle_recv\n    self._run_callback(callback, msg)\n  File \"c:\\users\\ichis\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\zmq\\eventloop\\zmqstream.py\", line 414, in _run_callback\n    callback(*args, **kwargs)\n  File \"c:\\users\\ichis\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\tornado\\stack_context.py\", line 277, in null_wrapper\n    return fn(*args, **kwargs)\n  File \"c:\\users\\ichis\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 283, in dispatcher\n    return self.dispatch_shell(stream, msg)\n  File \"c:\\users\\ichis\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 235, in dispatch_shell\n    handler(stream, idents, msg)\n  File \"c:\\users\\ichis\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 399, in execute_request\n    user_expressions, allow_stdin)\n  File \"c:\\users\\ichis\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\ipykernel\\ipkernel.py\", line 196, in do_execute\n    res = shell.run_cell(code, store_history=store_history, silent=silent)\n  File \"c:\\users\\ichis\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\ipykernel\\zmqshell.py\", line 533, in run_cell\n    return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)\n  File \"c:\\users\\ichis\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2698, in run_cell\n    interactivity=interactivity, compiler=compiler, result=result)\n  File \"c:\\users\\ichis\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2802, in run_ast_nodes\n    if self.run_code(code, result):\n  File \"c:\\users\\ichis\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2862, in run_code\n    exec(code_obj, self.user_global_ns, self.user_ns)\n  File \"<ipython-input-3-5ed998c24c13>\", line 21, in <module>\n    estimator.fit(input_fn=input_fn, steps=1000)# Here we evaluate how well our model did.\n  File \"c:\\users\\ichis\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\python\\util\\deprecation.py\", line 289, in new_func\n    return func(*args, **kwargs)\n  File \"c:\\users\\ichis\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\contrib\\learn\\python\\learn\\estimators\\estimator.py\", line 455, in fit\n    loss = self._train_model(input_fn=input_fn, hooks=hooks)\n  File \"c:\\users\\ichis\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\contrib\\learn\\python\\learn\\estimators\\estimator.py\", line 955, in _train_model\n    model_fn_ops = self._get_train_ops(features, labels)\n  File \"c:\\users\\ichis\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\contrib\\learn\\python\\learn\\estimators\\estimator.py\", line 1162, in _get_train_ops\n    return self._call_model_fn(features, labels, model_fn_lib.ModeKeys.TRAIN)\n  File \"c:\\users\\ichis\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\contrib\\learn\\python\\learn\\estimators\\estimator.py\", line 1133, in _call_model_fn\n    model_fn_results = self._model_fn(features, labels, **kwargs)\n  File \"c:\\users\\ichis\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\contrib\\learn\\python\\learn\\estimators\\linear.py\", line 164, in _linear_model_fn\n    scope=scope)\n  File \"c:\\users\\ichis\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\contrib\\layers\\python\\layers\\feature_column_ops.py\", line 513, in weighted_sum_from_feature_columns\n    predictions = math_ops.matmul(tensor, variable[0], name='matmul')\n  File \"c:\\users\\ichis\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\python\\ops\\math_ops.py\", line 1816, in matmul\n    a, b, transpose_a=transpose_a, transpose_b=transpose_b, name=name)\n  File \"c:\\users\\ichis\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\python\\ops\\gen_math_ops.py\", line 1217, in _mat_mul\n    transpose_b=transpose_b, name=name)\n  File \"c:\\users\\ichis\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\python\\framework\\op_def_library.py\", line 767, in apply_op\n    op_def=op_def)\n  File \"c:\\users\\ichis\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py\", line 2506, in create_op\n    original_op=self._default_original_op, op_def=op_def)\n  File \"c:\\users\\ichis\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py\", line 1269, in __init__\n    self._traceback = _extract_stack()\n\nInternalError (see above for traceback): Blas GEMV launch failed:  m=1, n=4\n\t [[Node: linear/linear/x/matmul = MatMul[T=DT_FLOAT, transpose_a=false, transpose_b=false, _device=\"/job:localhost/replica:0/task:0/gpu:0\"](linear/linear/x/ExpandDims, linear/x/weight)]]\n\t [[Node: linear/regression_head/train_op/Ftrl/update/_92 = _Recv[client_terminated=false, recv_device=\"/job:localhost/replica:0/task:0/cpu:0\", send_device=\"/job:localhost/replica:0/task:0/gpu:0\", send_device_incarnation=1, tensor_name=\"edge_80_linear/regression_head/train_op/Ftrl/update\", tensor_type=DT_FLOAT, _device=\"/job:localhost/replica:0/task:0/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:\\users\\ichis\\anaconda3\\envs\\tensorflow\\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   1138\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1139\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   1140\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\\ichis\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_run_fn\u001b[1;34m(session, feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[0;32m   1120\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-> 1121\u001b[1;33m                                  status, run_metadata)\n\u001b[0m\u001b[0;32m   1122\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\ichis\\anaconda3\\envs\\tensorflow\\lib\\contextlib.py\u001b[0m in \u001b[0;36m__exit__\u001b[1;34m(self, type, value, traceback)\u001b[0m\n\u001b[0;32m     65\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 66\u001b[1;33m                 \u001b[0mnext\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgen\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;32mexcept\u001b[0m \u001b[0mStopIteration\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\ichis\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\python\\framework\\errors_impl.py\u001b[0m in \u001b[0;36mraise_exception_on_not_ok_status\u001b[1;34m()\u001b[0m\n\u001b[0;32m    465\u001b[0m           \u001b[0mcompat\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpywrap_tensorflow\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTF_Message\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--> 466\u001b[1;33m           pywrap_tensorflow.TF_GetCode(status))\n\u001b[0m\u001b[0;32m    467\u001b[0m   \u001b[1;32mfinally\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=1, n=4\n\t [[Node: linear/linear/x/matmul = MatMul[T=DT_FLOAT, transpose_a=false, transpose_b=false, _device=\"/job:localhost/replica:0/task:0/gpu:0\"](linear/linear/x/ExpandDims, linear/x/weight)]]\n\t [[Node: linear/regression_head/train_op/Ftrl/update/_92 = _Recv[client_terminated=false, recv_device=\"/job:localhost/replica:0/task:0/cpu:0\", send_device=\"/job:localhost/replica:0/task:0/gpu:0\", send_device_incarnation=1, tensor_name=\"edge_80_linear/regression_head/train_op/Ftrl/update\", tensor_type=DT_FLOAT, _device=\"/job:localhost/replica:0/task:0/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-3-5ed998c24c13>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     19\u001b[0m     {\"x\":x_eval}, y_eval, batch_size=4, num_epochs=1000)# We can invoke 1000 training steps by invoking the  method and passing the\n\u001b[0;32m     20\u001b[0m \u001b[1;31m# training data set.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 21\u001b[1;33m \u001b[0mestimator\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput_fn\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0minput_fn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msteps\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1000\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;31m# Here we evaluate how well our model did.\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     22\u001b[0m \u001b[0mtrain_loss\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mestimator\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput_fn\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0minput_fn\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     23\u001b[0m \u001b[0meval_loss\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mestimator\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput_fn\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0meval_input_fn\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\ichis\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\python\\util\\deprecation.py\u001b[0m in \u001b[0;36mnew_func\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    287\u001b[0m             \u001b[1;34m'in a future version'\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mdate\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;32melse\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;34m'after %s'\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0mdate\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    288\u001b[0m             instructions)\n\u001b[1;32m--> 289\u001b[1;33m       \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\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[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    290\u001b[0m     return tf_decorator.make_decorator(func, new_func, 'deprecated',\n\u001b[0;32m    291\u001b[0m                                        _add_deprecated_arg_notice_to_docstring(\n",
      "\u001b[1;32mc:\\users\\ichis\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\contrib\\learn\\python\\learn\\estimators\\estimator.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, x, y, input_fn, steps, batch_size, monitors, max_steps)\u001b[0m\n\u001b[0;32m    453\u001b[0m       \u001b[0mhooks\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbasic_session_run_hooks\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mStopAtStepHook\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msteps\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmax_steps\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    454\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 455\u001b[1;33m     \u001b[0mloss\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_train_model\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput_fn\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0minput_fn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhooks\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mhooks\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    456\u001b[0m     \u001b[0mlogging\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Loss for final step: %s.'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mloss\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    457\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\ichis\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\contrib\\learn\\python\\learn\\estimators\\estimator.py\u001b[0m in \u001b[0;36m_train_model\u001b[1;34m(self, input_fn, hooks)\u001b[0m\n\u001b[0;32m   1005\u001b[0m         \u001b[0mloss\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1006\u001b[0m         \u001b[1;32mwhile\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mmon_sess\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshould_stop\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-> 1007\u001b[1;33m           \u001b[0m_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mloss\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmon_sess\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mmodel_fn_ops\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtrain_op\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmodel_fn_ops\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloss\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   1008\u001b[0m       \u001b[0msummary_io\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSummaryWriterCache\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclear\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1009\u001b[0m       \u001b[1;32mreturn\u001b[0m \u001b[0mloss\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\ichis\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\python\\training\\monitored_session.py\u001b[0m in \u001b[0;36mrun\u001b[1;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[0;32m    503\u001b[0m                           \u001b[0mfeed_dict\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfeed_dict\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    504\u001b[0m                           \u001b[0moptions\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 505\u001b[1;33m                           run_metadata=run_metadata)\n\u001b[0m\u001b[0;32m    506\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    507\u001b[0m   \u001b[1;32mdef\u001b[0m \u001b[0mshould_stop\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;32mc:\\users\\ichis\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\python\\training\\monitored_session.py\u001b[0m in \u001b[0;36mrun\u001b[1;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[0;32m    840\u001b[0m                               \u001b[0mfeed_dict\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfeed_dict\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    841\u001b[0m                               \u001b[0moptions\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 842\u001b[1;33m                               run_metadata=run_metadata)\n\u001b[0m\u001b[0;32m    843\u001b[0m       \u001b[1;32mexcept\u001b[0m \u001b[0m_PREEMPTION_ERRORS\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[0;32m    844\u001b[0m         logging.info('An error was raised. This may be due to a preemption in '\n",
      "\u001b[1;32mc:\\users\\ichis\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\python\\training\\monitored_session.py\u001b[0m in \u001b[0;36mrun\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    796\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    797\u001b[0m   \u001b[1;32mdef\u001b[0m \u001b[0mrun\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\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[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 798\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_sess\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun\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[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    799\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    800\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\ichis\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\python\\training\\monitored_session.py\u001b[0m in \u001b[0;36mrun\u001b[1;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[0;32m    950\u001b[0m                                   \u001b[0mfeed_dict\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfeed_dict\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    951\u001b[0m                                   \u001b[0moptions\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 952\u001b[1;33m                                   run_metadata=run_metadata)\n\u001b[0m\u001b[0;32m    953\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    954\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_hooks\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\ichis\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\python\\training\\monitored_session.py\u001b[0m in \u001b[0;36mrun\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    796\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    797\u001b[0m   \u001b[1;32mdef\u001b[0m \u001b[0mrun\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\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[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 798\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_sess\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun\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[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    799\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    800\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\ichis\\anaconda3\\envs\\tensorflow\\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    787\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    788\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[1;32m--> 789\u001b[1;33m                          run_metadata_ptr)\n\u001b[0m\u001b[0;32m    790\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    791\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\\ichis\\anaconda3\\envs\\tensorflow\\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    995\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    996\u001b[0m       results = self._do_run(handle, final_targets, final_fetches,\n\u001b[1;32m--> 997\u001b[1;33m                              feed_dict_string, options, run_metadata)\n\u001b[0m\u001b[0;32m    998\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    999\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\\ichis\\anaconda3\\envs\\tensorflow\\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   1130\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   1131\u001b[0m       return self._do_call(_run_fn, self._session, feed_dict, fetch_list,\n\u001b[1;32m-> 1132\u001b[1;33m                            target_list, options, run_metadata)\n\u001b[0m\u001b[0;32m   1133\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1134\u001b[0m       return self._do_call(_prun_fn, self._session, handle, feed_dict,\n",
      "\u001b[1;32mc:\\users\\ichis\\anaconda3\\envs\\tensorflow\\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   1150\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   1151\u001b[0m           \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1152\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   1153\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1154\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=1, n=4\n\t [[Node: linear/linear/x/matmul = MatMul[T=DT_FLOAT, transpose_a=false, transpose_b=false, _device=\"/job:localhost/replica:0/task:0/gpu:0\"](linear/linear/x/ExpandDims, linear/x/weight)]]\n\t [[Node: linear/regression_head/train_op/Ftrl/update/_92 = _Recv[client_terminated=false, recv_device=\"/job:localhost/replica:0/task:0/cpu:0\", send_device=\"/job:localhost/replica:0/task:0/gpu:0\", send_device_incarnation=1, tensor_name=\"edge_80_linear/regression_head/train_op/Ftrl/update\", tensor_type=DT_FLOAT, _device=\"/job:localhost/replica:0/task:0/cpu:0\"]()]]\n\nCaused by op 'linear/linear/x/matmul', defined at:\n  File \"c:\\users\\ichis\\anaconda3\\envs\\tensorflow\\lib\\runpy.py\", line 193, in _run_module_as_main\n    \"__main__\", mod_spec)\n  File \"c:\\users\\ichis\\anaconda3\\envs\\tensorflow\\lib\\runpy.py\", line 85, in _run_code\n    exec(code, run_globals)\n  File \"c:\\users\\ichis\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\ipykernel_launcher.py\", line 16, in <module>\n    app.launch_new_instance()\n  File \"c:\\users\\ichis\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\traitlets\\config\\application.py\", line 658, in launch_instance\n    app.start()\n  File \"c:\\users\\ichis\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\ipykernel\\kernelapp.py\", line 477, in start\n    ioloop.IOLoop.instance().start()\n  File \"c:\\users\\ichis\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\zmq\\eventloop\\ioloop.py\", line 177, in start\n    super(ZMQIOLoop, self).start()\n  File \"c:\\users\\ichis\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\tornado\\ioloop.py\", line 888, in start\n    handler_func(fd_obj, events)\n  File \"c:\\users\\ichis\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\tornado\\stack_context.py\", line 277, in null_wrapper\n    return fn(*args, **kwargs)\n  File \"c:\\users\\ichis\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\zmq\\eventloop\\zmqstream.py\", line 440, in _handle_events\n    self._handle_recv()\n  File \"c:\\users\\ichis\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\zmq\\eventloop\\zmqstream.py\", line 472, in _handle_recv\n    self._run_callback(callback, msg)\n  File \"c:\\users\\ichis\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\zmq\\eventloop\\zmqstream.py\", line 414, in _run_callback\n    callback(*args, **kwargs)\n  File \"c:\\users\\ichis\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\tornado\\stack_context.py\", line 277, in null_wrapper\n    return fn(*args, **kwargs)\n  File \"c:\\users\\ichis\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 283, in dispatcher\n    return self.dispatch_shell(stream, msg)\n  File \"c:\\users\\ichis\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 235, in dispatch_shell\n    handler(stream, idents, msg)\n  File \"c:\\users\\ichis\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 399, in execute_request\n    user_expressions, allow_stdin)\n  File \"c:\\users\\ichis\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\ipykernel\\ipkernel.py\", line 196, in do_execute\n    res = shell.run_cell(code, store_history=store_history, silent=silent)\n  File \"c:\\users\\ichis\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\ipykernel\\zmqshell.py\", line 533, in run_cell\n    return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)\n  File \"c:\\users\\ichis\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2698, in run_cell\n    interactivity=interactivity, compiler=compiler, result=result)\n  File \"c:\\users\\ichis\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2802, in run_ast_nodes\n    if self.run_code(code, result):\n  File \"c:\\users\\ichis\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2862, in run_code\n    exec(code_obj, self.user_global_ns, self.user_ns)\n  File \"<ipython-input-3-5ed998c24c13>\", line 21, in <module>\n    estimator.fit(input_fn=input_fn, steps=1000)# Here we evaluate how well our model did.\n  File \"c:\\users\\ichis\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\python\\util\\deprecation.py\", line 289, in new_func\n    return func(*args, **kwargs)\n  File \"c:\\users\\ichis\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\contrib\\learn\\python\\learn\\estimators\\estimator.py\", line 455, in fit\n    loss = self._train_model(input_fn=input_fn, hooks=hooks)\n  File \"c:\\users\\ichis\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\contrib\\learn\\python\\learn\\estimators\\estimator.py\", line 955, in _train_model\n    model_fn_ops = self._get_train_ops(features, labels)\n  File \"c:\\users\\ichis\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\contrib\\learn\\python\\learn\\estimators\\estimator.py\", line 1162, in _get_train_ops\n    return self._call_model_fn(features, labels, model_fn_lib.ModeKeys.TRAIN)\n  File \"c:\\users\\ichis\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\contrib\\learn\\python\\learn\\estimators\\estimator.py\", line 1133, in _call_model_fn\n    model_fn_results = self._model_fn(features, labels, **kwargs)\n  File \"c:\\users\\ichis\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\contrib\\learn\\python\\learn\\estimators\\linear.py\", line 164, in _linear_model_fn\n    scope=scope)\n  File \"c:\\users\\ichis\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\contrib\\layers\\python\\layers\\feature_column_ops.py\", line 513, in weighted_sum_from_feature_columns\n    predictions = math_ops.matmul(tensor, variable[0], name='matmul')\n  File \"c:\\users\\ichis\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\python\\ops\\math_ops.py\", line 1816, in matmul\n    a, b, transpose_a=transpose_a, transpose_b=transpose_b, name=name)\n  File \"c:\\users\\ichis\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\python\\ops\\gen_math_ops.py\", line 1217, in _mat_mul\n    transpose_b=transpose_b, name=name)\n  File \"c:\\users\\ichis\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\python\\framework\\op_def_library.py\", line 767, in apply_op\n    op_def=op_def)\n  File \"c:\\users\\ichis\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py\", line 2506, in create_op\n    original_op=self._default_original_op, op_def=op_def)\n  File \"c:\\users\\ichis\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py\", line 1269, in __init__\n    self._traceback = _extract_stack()\n\nInternalError (see above for traceback): Blas GEMV launch failed:  m=1, n=4\n\t [[Node: linear/linear/x/matmul = MatMul[T=DT_FLOAT, transpose_a=false, transpose_b=false, _device=\"/job:localhost/replica:0/task:0/gpu:0\"](linear/linear/x/ExpandDims, linear/x/weight)]]\n\t [[Node: linear/regression_head/train_op/Ftrl/update/_92 = _Recv[client_terminated=false, recv_device=\"/job:localhost/replica:0/task:0/cpu:0\", send_device=\"/job:localhost/replica:0/task:0/gpu:0\", send_device_incarnation=1, tensor_name=\"edge_80_linear/regression_head/train_op/Ftrl/update\", tensor_type=DT_FLOAT, _device=\"/job:localhost/replica:0/task:0/cpu:0\"]()]]\n"
     ]
    }
   ],
   "source": [
    "# other types of columns that are more complicated and useful.\n",
    "features = [tf.contrib.layers.real_valued_column(\"x\", dimension=1)]# An estimator is the front end to invoke training (fitting) and evaluation\n",
    "# (inference). There are many predefined types like linear regression,\n",
    "# logistic regression, linear classification, logistic classification, and\n",
    "# many neural network classifiers and regressors. The following code\n",
    "# provides an estimator that does linear regression.\n",
    "estimator = tf.contrib.learn.LinearRegressor(feature_columns=features)# TensorFlow provides many helper methods to read and set up data sets.\n",
    "# Here we use two data sets: one for training and one for evaluation\n",
    "# We have to tell the function how many batches\n",
    "# of data (num_epochs) we want and how big each batch should be.\n",
    "x_train = np.array([1., 2., 3., 4.])\n",
    "y_train = np.array([0., -1., -2., -3.])\n",
    "x_eval = np.array([2., 5., 8., 1.])\n",
    "y_eval = np.array([-1.01, -4.1, -7, 0.])\n",
    "input_fn = tf.contrib.learn.io.numpy_input_fn({\"x\":x_train}, y_train,\n",
    "                                              batch_size=4,\n",
    "                                              num_epochs=1000)\n",
    "eval_input_fn = tf.contrib.learn.io.numpy_input_fn(\n",
    "    {\"x\":x_eval}, y_eval, batch_size=4, num_epochs=1000)# We can invoke 1000 training steps by invoking the  method and passing the\n",
    "# training data set.\n",
    "estimator.fit(input_fn=input_fn, steps=1000)# Here we evaluate how well our model did.\n",
    "train_loss = estimator.evaluate(input_fn=input_fn)\n",
    "eval_loss = estimator.evaluate(input_fn=eval_input_fn)\n",
    "print(\"train loss: %r\"% train_loss)\n",
    "print(\"eval loss: %r\"% eval_loss)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lol that worked well. Note to self. Don't do that. \n"
   ]
  }
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