Card_predictor.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Dimension 1 in both shapes must be equal, but are 81 and 12. Shapes are [64,81] and [64,12]. for 'Assign_18' (op: 'Assign') with input shapes: [64,81], [64,12].",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mInvalidArgumentError\u001b[0m                      Traceback (most recent call last)",
      "\u001b[0;32m~/miniconda3/lib/python3.6/site-packages/tensorflow/python/framework/common_shapes.py\u001b[0m in \u001b[0;36m_call_cpp_shape_fn_impl\u001b[0;34m(op, input_tensors_needed, input_tensors_as_shapes_needed, require_shape_fn)\u001b[0m\n\u001b[1;32m    685\u001b[0m           \u001b[0mgraph_def_version\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnode_def_str\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput_shapes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput_tensors\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 686\u001b[0;31m           input_tensors_as_shapes, status)\n\u001b[0m\u001b[1;32m    687\u001b[0m   \u001b[0;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mInvalidArgumentError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/lib/python3.6/site-packages/tensorflow/python/framework/errors_impl.py\u001b[0m in \u001b[0;36m__exit__\u001b[0;34m(self, type_arg, value_arg, traceback_arg)\u001b[0m\n\u001b[1;32m    515\u001b[0m             \u001b[0mcompat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mc_api\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_Message\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstatus\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstatus\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 516\u001b[0;31m             c_api.TF_GetCode(self.status.status))\n\u001b[0m\u001b[1;32m    517\u001b[0m     \u001b[0;31m# Delete the underlying status object from memory otherwise it stays alive\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mInvalidArgumentError\u001b[0m: Dimension 1 in both shapes must be equal, but are 81 and 12. Shapes are [64,81] and [64,12]. for 'Assign_18' (op: 'Assign') with input shapes: [64,81], [64,12].",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-2-d95dc3ba1fb3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m    126\u001b[0m               metrics=['accuracy'])\n\u001b[1;32m    127\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 128\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_weights\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    129\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    130\u001b[0m \u001b[0;31m# Predict the image\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/lib/python3.6/site-packages/keras/models.py\u001b[0m in \u001b[0;36mload_weights\u001b[0;34m(self, filepath, by_name, skip_mismatch, reshape)\u001b[0m\n\u001b[1;32m    736\u001b[0m                                                               reshape=reshape)\n\u001b[1;32m    737\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 738\u001b[0;31m                 \u001b[0mtopology\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_weights_from_hdf5_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlayers\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreshape\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    739\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    740\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0msave_weights\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilepath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moverwrite\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/lib/python3.6/site-packages/keras/engine/topology.py\u001b[0m in \u001b[0;36mload_weights_from_hdf5_group\u001b[0;34m(f, layers, reshape)\u001b[0m\n\u001b[1;32m   3380\u001b[0m                              ' elements.')\n\u001b[1;32m   3381\u001b[0m         \u001b[0mweight_value_tuples\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msymbolic_weights\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mweight_values\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3382\u001b[0;31m     \u001b[0mK\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbatch_set_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mweight_value_tuples\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3383\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3384\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py\u001b[0m in \u001b[0;36mbatch_set_value\u001b[0;34m(tuples)\u001b[0m\n\u001b[1;32m   2366\u001b[0m                 assign_placeholder = tf.placeholder(tf_dtype,\n\u001b[1;32m   2367\u001b[0m                                                     shape=value.shape)\n\u001b[0;32m-> 2368\u001b[0;31m                 \u001b[0massign_op\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0massign\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0massign_placeholder\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2369\u001b[0m                 \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_assign_placeholder\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0massign_placeholder\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2370\u001b[0m                 \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_assign_op\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0massign_op\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/lib/python3.6/site-packages/tensorflow/python/ops/variables.py\u001b[0m in \u001b[0;36massign\u001b[0;34m(self, value, use_locking)\u001b[0m\n\u001b[1;32m    597\u001b[0m       \u001b[0mthe\u001b[0m \u001b[0massignment\u001b[0m \u001b[0mhas\u001b[0m \u001b[0mcompleted\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    598\u001b[0m     \"\"\"\n\u001b[0;32m--> 599\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mstate_ops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0massign\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_variable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0muse_locking\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0muse_locking\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    600\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    601\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0massign_add\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdelta\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0muse_locking\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/lib/python3.6/site-packages/tensorflow/python/ops/state_ops.py\u001b[0m in \u001b[0;36massign\u001b[0;34m(ref, value, validate_shape, use_locking, name)\u001b[0m\n\u001b[1;32m    278\u001b[0m     return gen_state_ops.assign(\n\u001b[1;32m    279\u001b[0m         \u001b[0mref\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0muse_locking\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0muse_locking\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 280\u001b[0;31m         validate_shape=validate_shape)\n\u001b[0m\u001b[1;32m    281\u001b[0m   \u001b[0;32mreturn\u001b[0m \u001b[0mref\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0massign\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    282\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/lib/python3.6/site-packages/tensorflow/python/ops/gen_state_ops.py\u001b[0m in \u001b[0;36massign\u001b[0;34m(ref, value, validate_shape, use_locking, name)\u001b[0m\n\u001b[1;32m     56\u001b[0m     _, _, _op = _op_def_lib._apply_op_helper(\n\u001b[1;32m     57\u001b[0m         \u001b[0;34m\"Assign\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mref\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mref\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalidate_shape\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvalidate_shape\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 58\u001b[0;31m         use_locking=use_locking, name=name)\n\u001b[0m\u001b[1;32m     59\u001b[0m     \u001b[0m_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_op\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     60\u001b[0m     \u001b[0m_inputs_flat\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_op\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py\u001b[0m in \u001b[0;36m_apply_op_helper\u001b[0;34m(self, op_type_name, name, **keywords)\u001b[0m\n\u001b[1;32m    785\u001b[0m         op = g.create_op(op_type_name, inputs, output_types, name=scope,\n\u001b[1;32m    786\u001b[0m                          \u001b[0minput_types\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minput_types\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mattrs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mattr_protos\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 787\u001b[0;31m                          op_def=op_def)\n\u001b[0m\u001b[1;32m    788\u001b[0m       \u001b[0;32mreturn\u001b[0m \u001b[0moutput_structure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mop_def\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_stateful\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    789\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/lib/python3.6/site-packages/tensorflow/python/framework/ops.py\u001b[0m in \u001b[0;36mcreate_op\u001b[0;34m(self, op_type, inputs, dtypes, input_types, name, attrs, op_def, compute_shapes, compute_device)\u001b[0m\n\u001b[1;32m   3271\u001b[0m         op_def=op_def)\n\u001b[1;32m   3272\u001b[0m     self._create_op_helper(ret, compute_shapes=compute_shapes,\n\u001b[0;32m-> 3273\u001b[0;31m                            compute_device=compute_device)\n\u001b[0m\u001b[1;32m   3274\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mret\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3275\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/lib/python3.6/site-packages/tensorflow/python/framework/ops.py\u001b[0m in \u001b[0;36m_create_op_helper\u001b[0;34m(self, op, compute_shapes, compute_device)\u001b[0m\n\u001b[1;32m   3311\u001b[0m     \u001b[0;31m# compute_shapes argument.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3312\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_c_op\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mcompute_shapes\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3313\u001b[0;31m       \u001b[0mset_shapes_for_outputs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mop\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3314\u001b[0m     \u001b[0;31m# TODO(b/XXXX): move to Operation.__init__ once _USE_C_API flag is removed.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3315\u001b[0m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_add_op\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mop\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/lib/python3.6/site-packages/tensorflow/python/framework/ops.py\u001b[0m in \u001b[0;36mset_shapes_for_outputs\u001b[0;34m(op)\u001b[0m\n\u001b[1;32m   2499\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0m_set_shapes_for_outputs_c_api\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mop\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2500\u001b[0m   \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2501\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_set_shapes_for_outputs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mop\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2502\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2503\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/lib/python3.6/site-packages/tensorflow/python/framework/ops.py\u001b[0m in \u001b[0;36m_set_shapes_for_outputs\u001b[0;34m(op)\u001b[0m\n\u001b[1;32m   2472\u001b[0m       \u001b[0mshape_func\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_call_cpp_shape_fn_and_require_op\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2473\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2474\u001b[0;31m   \u001b[0mshapes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mshape_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mop\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2475\u001b[0m   \u001b[0;32mif\u001b[0m \u001b[0mshapes\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2476\u001b[0m     raise RuntimeError(\n",
      "\u001b[0;32m~/miniconda3/lib/python3.6/site-packages/tensorflow/python/framework/ops.py\u001b[0m in \u001b[0;36mcall_with_requiring\u001b[0;34m(op)\u001b[0m\n\u001b[1;32m   2402\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2403\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0mcall_with_requiring\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mop\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2404\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mcall_cpp_shape_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrequire_shape_fn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2405\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2406\u001b[0m   \u001b[0m_call_cpp_shape_fn_and_require_op\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcall_with_requiring\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/lib/python3.6/site-packages/tensorflow/python/framework/common_shapes.py\u001b[0m in \u001b[0;36mcall_cpp_shape_fn\u001b[0;34m(op, require_shape_fn)\u001b[0m\n\u001b[1;32m    625\u001b[0m     res = _call_cpp_shape_fn_impl(op, input_tensors_needed,\n\u001b[1;32m    626\u001b[0m                                   \u001b[0minput_tensors_as_shapes_needed\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 627\u001b[0;31m                                   require_shape_fn)\n\u001b[0m\u001b[1;32m    628\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mres\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    629\u001b[0m       \u001b[0;31m# Handles the case where _call_cpp_shape_fn_impl calls unknown_shape(op).\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/lib/python3.6/site-packages/tensorflow/python/framework/common_shapes.py\u001b[0m in \u001b[0;36m_call_cpp_shape_fn_impl\u001b[0;34m(op, input_tensors_needed, input_tensors_as_shapes_needed, require_shape_fn)\u001b[0m\n\u001b[1;32m    689\u001b[0m       \u001b[0mmissing_shape_fn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    690\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 691\u001b[0;31m       \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    692\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    693\u001b[0m   \u001b[0;32mif\u001b[0m \u001b[0mmissing_shape_fn\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Dimension 1 in both shapes must be equal, but are 81 and 12. Shapes are [64,81] and [64,12]. for 'Assign_18' (op: 'Assign') with input shapes: [64,81], [64,12]."
     ]
    }
   ],
   "source": [
    "import keras\n",
    "from keras.preprocessing import image\n",
    "from keras.applications.xception import (Xception, preprocess_input, decode_predictions)\n",
    "from keras import models\n",
    "from keras.models import load_model\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Conv2D, MaxPooling2D\n",
    "from keras.layers import Activation, Dropout, Flatten, Dense\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os\n",
    "from os import listdir\n",
    "from PIL import Image\n",
    "\n",
    "class_list = ['1DGE',\n",
    " '1DGF',\n",
    " '1DGS',\n",
    " '1DPE',\n",
    " '1DPF',\n",
    " '1DPS',\n",
    " '1DRE',\n",
    " '1DRF',\n",
    " '1DRS',\n",
    " '1OGE',\n",
    " '1OGF',\n",
    " '1OGS',\n",
    " '1OPE',\n",
    " '1OPF',\n",
    " '1OPS',\n",
    " '1ORE',\n",
    " '1ORF',\n",
    " '1ORS',\n",
    " '1SGE',\n",
    " '1SGF',\n",
    " '1SGS',\n",
    " '1SPE',\n",
    " '1SPF',\n",
    " '1SPS',\n",
    " '1SRE',\n",
    " '1SRF',\n",
    " '1SRS',\n",
    " '2DGE',\n",
    " '2DGF',\n",
    " '2DGS',\n",
    " '2DPE',\n",
    " '2DPF',\n",
    " '2DPS',\n",
    " '2DRE',\n",
    " '2DRF',\n",
    " '2DRS',\n",
    " '2OGE',\n",
    " '2OGF',\n",
    " '2OGS',\n",
    " '2OPE',\n",
    " '2OPF',\n",
    " '2OPS',\n",
    " '2ORE',\n",
    " '2ORF',\n",
    " '2ORS',\n",
    " '2SGE',\n",
    " '2SGF',\n",
    " '2SGS',\n",
    " '2SPE',\n",
    " '2SPF',\n",
    " '2SPS',\n",
    " '2SRE',\n",
    " '2SRF',\n",
    " '2SRS',\n",
    " '3DGE',\n",
    " '3DGF',\n",
    " '3DGS',\n",
    " '3DPE',\n",
    " '3DPF',\n",
    " '3DPS',\n",
    " '3DRE',\n",
    " '3DRF',\n",
    " '3DRS',\n",
    " '3OGE',\n",
    " '3OGF',\n",
    " '3OGS',\n",
    " '3OPE',\n",
    " '3OPF',\n",
    " '3OPS',\n",
    " '3ORE',\n",
    " '3ORF',\n",
    " '3ORS',\n",
    " '3SGE',\n",
    " '3SGF',\n",
    " '3SGS',\n",
    " '3SPE',\n",
    " '3SPF',\n",
    " '3SPS',\n",
    " '3SRE',\n",
    " '3SRF',\n",
    " '3SRS']\n",
    "\n",
    "model_path = 'static/model/POC_12.h5'\n",
    "image_size = (150, 150)\n",
    "folderpath = 'static/images/cropped'\n",
    "\n",
    "#Create Model and Load Weights\n",
    "\n",
    "model = Sequential()\n",
    "model.add(Conv2D(32, (3, 3), input_shape=(150, 150, 3)))\n",
    "model.add(Activation('relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "\n",
    "model.add(Conv2D(32, (3, 3)))\n",
    "model.add(Activation('relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "\n",
    "model.add(Conv2D(64, (3, 3)))\n",
    "model.add(Activation('relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "\n",
    "model.add(Flatten())\n",
    "model.add(Dense(64))\n",
    "model.add(Activation('relu'))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(81))\n",
    "model.add(Activation('softmax'))\n",
    "\n",
    "model.compile(loss='categorical_crossentropy',\n",
    "              optimizer='adam',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "model.load_weights(model_path)\n",
    "\n",
    "# Predict the image\n",
    "\n",
    "def predictor(image_path):\n",
    "    img = image.load_img(image_path, target_size=image_size)\n",
    "    x = image.img_to_array(img)\n",
    "    # print(x.shape)\n",
    "    x = np.expand_dims(x, axis=0)\n",
    "    x = preprocess_input(x)\n",
    "    predictions = model.predict(x)\n",
    "    # print(max(predictions[0]))\n",
    "    list_a = predictions[0]\n",
    "    list_b = class_list\n",
    "    zip_test = list(zip(list_a, list_b))\n",
    "    return max(zip_test)\n",
    "\n",
    "def find_cropped_images(folderpath):\n",
    "    images = [folderpath + '/' + v for v in os.listdir(folderpath) if v != '.DS_Store']\n",
    "    return images\n",
    "\n",
    "def loadImages(imagepath):\n",
    "    imagesList = imagepath\n",
    "    loadedImages = []\n",
    "    for image in imagesList:\n",
    "        img = Image.open(image)\n",
    "        loadedImages.append(img)\n",
    "    return loadedImages\n",
    "\n",
    "\n",
    "def prediction_tuples():\n",
    "     predictions = [predictor(png) for png in find_cropped_images(folderpath)]\n",
    "     return predictions\n",
    "\n",
    "board = prediction_tuples()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{0: {'number': '2', 'shape': 'S', 'color': 'G', 'shading': 'F'}, 1: {'number': '2', 'shape': 'D', 'color': 'R', 'shading': 'F'}, 2: {'number': '3', 'shape': 'O', 'color': 'G', 'shading': 'S'}, 3: {'number': '1', 'shape': 'O', 'color': 'P', 'shading': 'S'}, 4: {'number': '3', 'shape': 'O', 'color': 'G', 'shading': 'S'}, 5: {'number': '1', 'shape': 'O', 'color': 'G', 'shading': 'E'}, 6: {'number': '3', 'shape': 'D', 'color': 'R', 'shading': 'E'}, 7: {'number': '3', 'shape': 'D', 'color': 'R', 'shading': 'E'}, 8: {'number': '1', 'shape': 'O', 'color': 'R', 'shading': 'E'}, 9: {'number': '3', 'shape': 'O', 'color': 'G', 'shading': 'S'}, 10: {'number': '3', 'shape': 'O', 'color': 'R', 'shading': 'F'}, 11: {'number': '3', 'shape': 'O', 'color': 'G', 'shading': 'S'}}\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{0: {'color': 'G', 'number': '2', 'shading': 'F', 'shape': 'S'},\n",
       " 1: {'color': 'R', 'number': '2', 'shading': 'F', 'shape': 'D'},\n",
       " 2: {'color': 'G', 'number': '3', 'shading': 'S', 'shape': 'O'},\n",
       " 3: {'color': 'P', 'number': '1', 'shading': 'S', 'shape': 'O'},\n",
       " 4: {'color': 'G', 'number': '3', 'shading': 'S', 'shape': 'O'},\n",
       " 5: {'color': 'G', 'number': '1', 'shading': 'E', 'shape': 'O'},\n",
       " 6: {'color': 'R', 'number': '3', 'shading': 'E', 'shape': 'D'},\n",
       " 7: {'color': 'R', 'number': '3', 'shading': 'E', 'shape': 'D'},\n",
       " 8: {'color': 'R', 'number': '1', 'shading': 'E', 'shape': 'O'},\n",
       " 9: {'color': 'G', 'number': '3', 'shading': 'S', 'shape': 'O'},\n",
       " 10: {'color': 'R', 'number': '3', 'shading': 'F', 'shape': 'O'},\n",
       " 11: {'color': 'G', 'number': '3', 'shading': 'S', 'shape': 'O'}}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import unpack\n",
    "unpack.unpack_result(board)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "def unpack_result(cards):\n",
    "    predictions = []\n",
    "    for card in cards:\n",
    "        predictions.append(list(card[1]))\n",
    "    \n",
    "    board = {}\n",
    "    for i in range(0,len(cards)):\n",
    "        board[i] ={'number':predictions[i][0],\n",
    "                   'shape':predictions[i][1],\n",
    "                   'color':predictions[i][2],\n",
    "                   'shading':predictions[i][3]}\n",
    "   \n",
    "    return board"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = []\n",
    "for card in cards:\n",
    "    predictions.append(list(card[1]))\n",
    "print(predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{0: {'number': '2', 'shape': 'S', 'color': 'G', 'shading': 'F'}, 1: {'number': '2', 'shape': 'D', 'color': 'R', 'shading': 'F'}, 2: {'number': '3', 'shape': 'O', 'color': 'G', 'shading': 'S'}, 3: {'number': '1', 'shape': 'O', 'color': 'P', 'shading': 'S'}, 4: {'number': '3', 'shape': 'O', 'color': 'G', 'shading': 'S'}, 5: {'number': '1', 'shape': 'O', 'color': 'G', 'shading': 'E'}, 6: {'number': '3', 'shape': 'D', 'color': 'R', 'shading': 'E'}, 7: {'number': '3', 'shape': 'D', 'color': 'R', 'shading': 'E'}, 8: {'number': '1', 'shape': 'O', 'color': 'R', 'shading': 'E'}, 9: {'number': '3', 'shape': 'O', 'color': 'G', 'shading': 'S'}, 10: {'number': '3', 'shape': 'O', 'color': 'R', 'shading': 'F'}, 11: {'number': '3', 'shape': 'O', 'color': 'G', 'shading': 'S'}}\n"
     ]
    }
   ],
   "source": [
    "board = unpack.unpack_result(board)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import Gamelogic\n",
    "foundSet = Gamelogic.findSet(board)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['2SGF', '1OPS', '3DRE'],\n",
       " ['2SGF', '1OPS', '3DRE'],\n",
       " ['3OGS', '3OGS', '3OGS'],\n",
       " ['3OGS', '3OGS', '3OGS'],\n",
       " ['3OGS', '3OGS', '3OGS'],\n",
       " ['3OGS', '3OGS', '3OGS']]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "foundSet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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    "name": "ipython",
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   "file_extension": ".py",
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