recipenet.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install -U pip\n",
    "!pip install tensorflow-gpu==2.0.0-rc0\n",
    "!pip install matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "fWoaBTaNJTeV",
    "outputId": "7c0b055e-2ac2-4d4c-a7c3-2262c7b5ea1d",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.0.0-rc0\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from matplotlib import pyplot as plt\n",
    "import math\n",
    "import datetime\n",
    "import os\n",
    "from util import Recipes\n",
    "import random\n",
    "print(tf.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1035530,) (32027,)\n"
     ]
    }
   ],
   "source": [
    "r = Recipes()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 68
    },
    "colab_type": "code",
    "id": "7eDjUCjkJTex",
    "outputId": "1a32e93d-9732-433f-fd4e-c8674ebca510"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(27, ['pepper', 'butter', 'ground', 'cream', 'chicken', 'vegetable oil', 'unsalted butter', 'mustard', 'mushrooms', 'white wine', 'heavy cream', 'boneless', 'wine', 'ground pepper', 'chicken stock', 'dijon mustard', 'stock', 'freshly ground pepper', 'dry white wine', 'dill', 'chicken breast halves', 'boneless chicken', 'boneless chicken breast', 'boneless chicken breast halves', 'with skin', 'cremini mushrooms', 'cognac'])\n",
      "\n",
      "(5, ['white', 'vegetable', 'chicken breast', 'shallot', 'cremini'])\n"
     ]
    }
   ],
   "source": [
    "g = r.batch_iterator(r.train_recipes, 200)\n",
    "x, y = g.__next__()\n",
    "i = random.randint(0, 199)\n",
    "print(r.onehot_to_hr_recipe(x[i]))\n",
    "print()\n",
    "print(r.onehot_to_hr_recipe(y[i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "oN2685mdJTe0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_6 (Dense)              (None, 500)               1750500   \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_5 (LeakyReLU)    (None, 500)               0         \n",
      "_________________________________________________________________\n",
      "dense_7 (Dense)              (None, 330)               165330    \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_6 (LeakyReLU)    (None, 330)               0         \n",
      "_________________________________________________________________\n",
      "dense_8 (Dense)              (None, 300)               99300     \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_7 (LeakyReLU)    (None, 300)               0         \n",
      "_________________________________________________________________\n",
      "dense_9 (Dense)              (None, 330)               99330     \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_8 (LeakyReLU)    (None, 330)               0         \n",
      "_________________________________________________________________\n",
      "dense_10 (Dense)             (None, 500)               165500    \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_9 (LeakyReLU)    (None, 500)               0         \n",
      "_________________________________________________________________\n",
      "dense_11 (Dense)             (None, 3500)              1753500   \n",
      "=================================================================\n",
      "Total params: 4,033,460\n",
      "Trainable params: 4,033,460\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "batch_size = 256\n",
    "valid_batch_size = 512\n",
    "epochs = 28\n",
    "\n",
    "model = tf.keras.models.Sequential([tf.keras.layers.Dense(500, input_shape=(3500,)),\n",
    "                                    tf.keras.layers.LeakyReLU(),\n",
    "                                    \n",
    "                                    tf.keras.layers.Dense(330),\n",
    "                                    tf.keras.layers.LeakyReLU(),\n",
    "                                    \n",
    "                                    tf.keras.layers.Dense(300),\n",
    "                                    tf.keras.layers.LeakyReLU(),\n",
    "                                                                                                            \n",
    "                                    tf.keras.layers.Dense(330),\n",
    "                                    tf.keras.layers.LeakyReLU(),\n",
    "                                    \n",
    "                                    tf.keras.layers.Dense(500),\n",
    "                                    tf.keras.layers.LeakyReLU(),\n",
    "                                    \n",
    "                                    tf.keras.layers.Dense(len(r.ingredients), activation='sigmoid')\n",
    "                                   ])\n",
    "\n",
    "model.compile(optimizer='adam',\n",
    "              loss='categorical_crossentropy',\n",
    "              metrics=['categorical_accuracy'])\n",
    "\n",
    "model.summary()\n",
    "\n",
    "logdir = os.path.join(\"logs\", datetime.datetime.now().strftime(\"%Y-%m-%d-%H:%M:%S\"))\n",
    "tensorboard_callback = tf.keras.callbacks.TensorBoard(logdir, histogram_freq=1, update_freq=batch_size*64)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**note: up to ~0.43 validation categorical_accuracy is possible with the right architecture and by training for 4-8 epochs**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "xjj24AUTJTe4",
    "outputId": "97eb5f48-1382-44bc-fc8e-8373d103ec82"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/28\n",
      "4045/4045 [==============================] - 248s 61ms/step - loss: 9.7904 - categorical_accuracy: 0.2971 - val_loss: 8.5066 - val_categorical_accuracy: 0.3842\n",
      "Epoch 2/28\n",
      "4045/4045 [==============================] - 249s 62ms/step - loss: 8.2138 - categorical_accuracy: 0.3950 - val_loss: 8.0492 - val_categorical_accuracy: 0.4067\n",
      "Epoch 3/28\n",
      "  45/4045 [..............................] - ETA: 4:37 - loss: 7.9366 - categorical_accuracy: 0.4037"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0mTraceback (most recent call last)",
      "\u001b[0;32m<ipython-input-19-aef0ee66b9bb>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      3\u001b[0m                               \u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_batch_count\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_recipes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\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      4\u001b[0m                               \u001b[0mvalidation_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbatch_iterator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtest_recipes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvalid_batch_size\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----> 5\u001b[0;31m                               \u001b[0mvalidation_steps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_batch_count\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtest_recipes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalid_batch_size\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[0m\u001b[1;32m      6\u001b[0m                               \u001b[0;31m#callbacks=[tensorboard_callback]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m                              )\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/keras/engine/training.py\u001b[0m in \u001b[0;36mfit_generator\u001b[0;34m(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, validation_freq, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)\u001b[0m\n\u001b[1;32m   1301\u001b[0m         \u001b[0mshuffle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mshuffle\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1302\u001b[0m         \u001b[0minitial_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minitial_epoch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1303\u001b[0;31m         steps_name='steps_per_epoch')\n\u001b[0m\u001b[1;32m   1304\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1305\u001b[0m   def evaluate_generator(self,\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/keras/engine/training_generator.py\u001b[0m in \u001b[0;36mmodel_iteration\u001b[0;34m(model, data, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, validation_freq, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch, mode, batch_size, steps_name, **kwargs)\u001b[0m\n\u001b[1;32m    263\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    264\u001b[0m       \u001b[0mis_deferred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_is_compiled\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 265\u001b[0;31m       \u001b[0mbatch_outs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbatch_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mbatch_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    266\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_outs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\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    267\u001b[0m         \u001b[0mbatch_outs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mbatch_outs\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/keras/engine/training.py\u001b[0m in \u001b[0;36mtrain_on_batch\u001b[0;34m(self, x, y, sample_weight, class_weight, reset_metrics)\u001b[0m\n\u001b[1;32m    977\u001b[0m       outputs = training_v2_utils.train_on_batch(\n\u001b[1;32m    978\u001b[0m           \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 979\u001b[0;31m           class_weight=class_weight, reset_metrics=reset_metrics)\n\u001b[0m\u001b[1;32m    980\u001b[0m       outputs = (outputs['total_loss'] + outputs['output_losses'] +\n\u001b[1;32m    981\u001b[0m                  outputs['metrics'])\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/keras/engine/training_v2_utils.py\u001b[0m in \u001b[0;36mtrain_on_batch\u001b[0;34m(model, x, y, sample_weight, class_weight, reset_metrics)\u001b[0m\n\u001b[1;32m    262\u001b[0m       \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    263\u001b[0m       \u001b[0msample_weights\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msample_weights\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 264\u001b[0;31m       output_loss_metrics=model._output_loss_metrics)\n\u001b[0m\u001b[1;32m    265\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    266\u001b[0m   \u001b[0;32mif\u001b[0m \u001b[0mreset_metrics\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/keras/engine/training_eager.py\u001b[0m in \u001b[0;36mtrain_on_batch\u001b[0;34m(model, inputs, targets, sample_weights, output_loss_metrics)\u001b[0m\n\u001b[1;32m    309\u001b[0m           \u001b[0msample_weights\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msample_weights\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    310\u001b[0m           \u001b[0mtraining\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--> 311\u001b[0;31m           output_loss_metrics=output_loss_metrics))\n\u001b[0m\u001b[1;32m    312\u001b[0m   \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mouts\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\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    313\u001b[0m     \u001b[0mouts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mouts\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/keras/engine/training_eager.py\u001b[0m in \u001b[0;36m_process_single_batch\u001b[0;34m(model, inputs, targets, output_loss_metrics, sample_weights, training)\u001b[0m\n\u001b[1;32m    250\u001b[0m               \u001b[0moutput_loss_metrics\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moutput_loss_metrics\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    251\u001b[0m               \u001b[0msample_weights\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msample_weights\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 252\u001b[0;31m               training=training))\n\u001b[0m\u001b[1;32m    253\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0mtotal_loss\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    254\u001b[0m         raise ValueError('The model cannot be run '\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/keras/engine/training_eager.py\u001b[0m in \u001b[0;36m_model_loss\u001b[0;34m(model, inputs, targets, output_loss_metrics, sample_weights, training)\u001b[0m\n\u001b[1;32m    129\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    130\u001b[0m   \u001b[0;32mif\u001b[0m \u001b[0mtargets\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 131\u001b[0;31m     \u001b[0mtargets\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtraining_utils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcast_if_floating_dtype_and_mismatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtargets\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mouts\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    132\u001b[0m   \u001b[0;31m# TODO(sallymatson/psv): check if we should do same mismatch fix for weights\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    133\u001b[0m   \u001b[0;32mif\u001b[0m \u001b[0msample_weights\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/keras/engine/training_utils.py\u001b[0m in \u001b[0;36mcast_if_floating_dtype_and_mismatch\u001b[0;34m(targets, outputs)\u001b[0m\n\u001b[1;32m   1243\u001b[0m   \u001b[0;32mfor\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtargets\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   1244\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtarget\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\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-> 1245\u001b[0;31m       \u001b[0mtarget\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconvert_to_tensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtarget\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1246\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1247\u001b[0m       \u001b[0mnew_targets\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcast_single_tensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtarget\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\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/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/framework/ops.py\u001b[0m in \u001b[0;36mconvert_to_tensor\u001b[0;34m(value, dtype, name, preferred_dtype, dtype_hint)\u001b[0m\n\u001b[1;32m   1182\u001b[0m   preferred_dtype = deprecation.deprecated_argument_lookup(\n\u001b[1;32m   1183\u001b[0m       \"dtype_hint\", dtype_hint, \"preferred_dtype\", preferred_dtype)\n\u001b[0;32m-> 1184\u001b[0;31m   \u001b[0;32mreturn\u001b[0m \u001b[0mconvert_to_tensor_v2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpreferred_dtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1185\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1186\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/framework/ops.py\u001b[0m in \u001b[0;36mconvert_to_tensor_v2\u001b[0;34m(value, dtype, dtype_hint, name)\u001b[0m\n\u001b[1;32m   1240\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[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1241\u001b[0m       \u001b[0mpreferred_dtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype_hint\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1242\u001b[0;31m       as_ref=False)\n\u001b[0m\u001b[1;32m   1243\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1244\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/framework/ops.py\u001b[0m in \u001b[0;36minternal_convert_to_tensor\u001b[0;34m(value, dtype, name, as_ref, preferred_dtype, ctx, accept_composite_tensors)\u001b[0m\n\u001b[1;32m   1294\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1295\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mret\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1296\u001b[0;31m       \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconversion_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\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[0mas_ref\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mas_ref\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1297\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1298\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mret\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNotImplemented\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/framework/tensor_conversion_registry.py\u001b[0m in \u001b[0;36m_default_conversion_function\u001b[0;34m(***failed resolving arguments***)\u001b[0m\n\u001b[1;32m     50\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_default_conversion_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mas_ref\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     51\u001b[0m   \u001b[0;32mdel\u001b[0m \u001b[0mas_ref\u001b[0m  \u001b[0;31m# Unused.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 52\u001b[0;31m   \u001b[0;32mreturn\u001b[0m \u001b[0mconstant_op\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconstant\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\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[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     53\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/framework/constant_op.py\u001b[0m in \u001b[0;36mconstant\u001b[0;34m(value, dtype, shape, name)\u001b[0m\n\u001b[1;32m    225\u001b[0m   \"\"\"\n\u001b[1;32m    226\u001b[0m   return _constant_impl(value, dtype, shape, name, verify_shape=False,\n\u001b[0;32m--> 227\u001b[0;31m                         allow_broadcast=True)\n\u001b[0m\u001b[1;32m    228\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    229\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/framework/constant_op.py\u001b[0m in \u001b[0;36m_constant_impl\u001b[0;34m(value, dtype, shape, name, verify_shape, allow_broadcast)\u001b[0m\n\u001b[1;32m    233\u001b[0m   \u001b[0mctx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontext\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    234\u001b[0m   \u001b[0;32mif\u001b[0m \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexecuting_eagerly\u001b[0m\u001b[0;34m(\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--> 235\u001b[0;31m     \u001b[0mt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconvert_to_eager_tensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mctx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    236\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mshape\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    237\u001b[0m       \u001b[0;32mreturn\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/framework/constant_op.py\u001b[0m in \u001b[0;36mconvert_to_eager_tensor\u001b[0;34m(value, ctx, dtype)\u001b[0m\n\u001b[1;32m     94\u001b[0m       \u001b[0mdtype\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdtypes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_datatype_enum\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     95\u001b[0m   \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensure_initialized\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---> 96\u001b[0;31m   \u001b[0;32mreturn\u001b[0m \u001b[0mops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mEagerTensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdevice_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     97\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     98\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "history = model.fit_generator(r.batch_iterator(r.train_recipes, batch_size=batch_size), \n",
    "                              epochs=epochs, \n",
    "                              steps_per_epoch=r.get_batch_count(r.train_recipes, batch_size), \n",
    "                              validation_data=r.batch_iterator(r.test_recipes, batch_size=valid_batch_size), \n",
    "                              validation_steps=r.get_batch_count(r.test_recipes, valid_batch_size),\n",
    "                              #callbacks=[tensorboard_callback]\n",
    "                             )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "tf.saved_model.save(model, \"recipenet_saved_model/1/\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def no_overlap(item, s):\n",
    "    for k in s:\n",
    "        if item in k or k in item:\n",
    "            return False\n",
    "    return True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['tortillas',\n",
       " 'a',\n",
       " 'flour tortillas',\n",
       " 'corn tortillas',\n",
       " 'tortilla chips',\n",
       " 'corn tortilla chips',\n",
       " 'flour tortilla',\n",
       " 'baked corn tortilla chips',\n",
       " 'tortilla shells',\n",
       " 'tortilla wraps']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r.match_ingr('tortilla')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 391
    },
    "colab_type": "code",
    "id": "UR8DctbuJTfE",
    "outputId": "dddd5670-234c-4017-f841-00c831592720"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['salt', 'sugar', 'rice', 'cucumber', 'nori', 'sushi rice'] -->\n",
      "\n",
      "(sushi rice) | 0.109 | .\n",
      "seaweed | 0.069 | .\n",
      "vinegar | 0.044 | .\n",
      "radish | 0.033 | .\n",
      "water | 0.029 | .\n",
      "wasabi | 0.026 | .\n",
      "dashi | 0.015 | .\n",
      "mirin | 0.015 | .\n",
      "(nori) | 0.015 | .\n",
      "sake | 0.014 | .\n",
      "black sesame seeds | 0.011 | .\n",
      "tofu | 0.010 | .\n",
      "(cucumber) | 0.009 | .\n",
      "sliced | 0.006 | .\n",
      "scallions | 0.006 | .\n",
      "(cucumbers) | 0.006 | .\n"
     ]
    }
   ],
   "source": [
    "i = r.labels_to_batch(['sushi rice', 'rice', 'cucumber', 'sugar', 'nori', 'salt'])\n",
    "#i = r.labels_to_batch(['rice', 'black beans', 'tortillas', 'flour tortillas', 'guacamole', 'peppers'])\n",
    "#i = r.labels_to_batch(['rice', 'oil', 'salt', 'pepper', 'spices', 'seasoning', 'vegetables', 'carrots', 'bell peppers', 'tomatoes'])\n",
    "#i = r.labels_to_batch(['butter', 'sugar', 'eggs', 'vanilla', 'flour', 'blueberries'])\n",
    "p = model.predict_on_batch(i)\n",
    "_, hr = r.onehot_to_hr_recipe(i[0]) \n",
    "\n",
    "print(hr, '-->\\n')\n",
    "\n",
    "n = 16\n",
    "added_ingr = list(reversed(r.ingredients[np.argsort(p[0])[-n:]]))\n",
    "added_ingr_p = list(reversed(p[0].numpy()[np.argsort(p[0])[-n:]]))\n",
    "\n",
    "n_c = 0\n",
    "for ingr, p in zip(added_ingr, added_ingr_p):\n",
    "    no_overlap_ = no_overlap(ingr, set(hr))\n",
    "    print(('' if no_overlap_ else '(')+ingr+('' if no_overlap_ else ')'), '|', str(p)[:5], '|', '.')\n",
    "    #if no_overlap_: n_c += 1\n",
    "    #if n_c > 6: break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "3_OHTg5VJTfK",
    "outputId": "6d4079fe-f11c-4098-fffe-5cf22c93f6e3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "284.8895"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g = r.batch_iterator(r.test_recipes, 128)\n",
    "x, y = g.__next__()\n",
    "p = model.predict_on_batch(x)\n",
    "np.sum(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "name": "recipenet.ipynb",
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  "kernelspec": {
   "display_name": "Python 3",
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  "pycharm": {
   "stem_cell": {
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