custom.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "TensorFlow version: 2.4.1\nEager execution: True\n"
    }
   ],
   "source": [
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf\n",
    "print(\"TensorFlow version: {}\".format(tf.__version__))\n",
    "print(\"Eager execution: {}\".format(tf.executing_eagerly()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Local copy of the dataset file: /home/paul/dev/intelligent_learn/data/train.csv\n"
    }
   ],
   "source": [
    "train_dataset_url = \"https://storage.googleapis.com/download.tensorflow.org/data/iris_training.csv\"\n",
    "\n",
    "#train_dataset_fp = tf.keras.utils.get_file(fname=os.path.basename(train_dataset_url),\n",
    "#                                          origin=train_dataset_url)\n",
    "train_dataset_fp = \"/home/paul/dev/intelligent_learn/data/train.csv\"\n",
    "print(\"Local copy of the dataset file: {}\".format(train_dataset_fp))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Features: ['sepal_length', 'sepal_width', 'petal_length', 'petal_width']\nLabel: species\n"
    }
   ],
   "source": [
    "# column order in CSV file\n",
    "column_names = ['sepal_length', 'sepal_width', 'petal_length', 'petal_width', 'species']\n",
    "\n",
    "feature_names = column_names[:-1]\n",
    "label_name = column_names[-1]\n",
    "\n",
    "print(\"Features: {}\".format(feature_names))\n",
    "print(\"Label: {}\".format(label_name))\n",
    "\n",
    "class_names = ['Iris setosa', 'Iris versicolor', 'Iris virginica']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "output_type": "error",
     "ename": "AttributeError",
     "evalue": "'PrefetchDataset' object has no attribute 'shape'",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-9-498f1d1d735e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      7\u001b[0m     \u001b[0mlabel_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlabel_name\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m     num_epochs=1)\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_dataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m: 'PrefetchDataset' object has no attribute 'shape'"
     ]
    }
   ],
   "source": [
    "batch_size = 32\n",
    "\n",
    "train_dataset = tf.data.experimental.make_csv_dataset(\n",
    "    train_dataset_fp,\n",
    "    batch_size,\n",
    "    column_names=column_names,\n",
    "    label_name=label_name,\n",
    "    num_epochs=1)\n",
    "print(train_dataset.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "features, labels = next(iter(train_dataset))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.scatter(features['petal_length'],\n",
    "            features['sepal_length'],\n",
    "            c=labels,\n",
    "            cmap='viridis')\n",
    "\n",
    "plt.xlabel(\"Petal length\")\n",
    "plt.ylabel(\"Sepal length\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pack_features_vector(features, labels):\n",
    "  \"\"\"Pack the features into a single array.\"\"\"\n",
    "  features = tf.stack(list(features.values()), axis=1)\n",
    "  return features, labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dataset = train_dataset.map(pack_features_vector)\n",
    "features, labels = next(iter(train_dataset))\n",
    "#print(train_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = tf.keras.Sequential([\n",
    "  tf.keras.layers.Dense(10, activation=tf.nn.relu, input_shape=(4,)),  # input shape required\n",
    "  #tf.keras.layers.Dense(10, activation=tf.nn.relu),\n",
    "  tf.keras.layers.Dense(3)\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "tf.Tensor(\n[[0.8373    0.6816    0.81112   0.9223   ]\n [0.737     0.624     0.8412    0.5323   ]\n [0.274     0.47      0.342     0.33123  ]\n [0.857     0.68115   0.6212    0.8123   ]\n [0.9274    0.847     0.7342    0.6633123]\n [0.57      0.115     0.212     0.123    ]\n [0.267     0.137     0.412     0.2323   ]\n [0.6273    0.98476   0.6312    0.6223   ]\n [0.373     0.16      0.1112    0.223    ]\n [0.836     0.9437    0.8312    0.681223 ]\n [0.9267    0.8137    0.6412    0.82323  ]\n [0.277     0.14      0.612     0.323    ]\n [0.273     0.476     0.312     0.223    ]\n [0.37      0.24      0.412     0.323    ]\n [0.36      0.437     0.312     0.1223   ]\n [0.6277    0.714     0.6612    0.8323   ]], shape=(16, 4), dtype=float32)\ntf.Tensor(\n[[ 1.1208105   0.7542406  -0.70457584]\n [ 0.8012574   0.61631966 -0.4988514 ]\n [ 0.4643574   0.16691351 -0.27278745]\n [ 1.0172127   0.66648054 -0.6357366 ]\n [ 0.9772919   0.6287073  -0.59844357]\n [ 0.25428152  0.44407302 -0.23357335]\n [ 0.31009716  0.31495538 -0.20279   ]\n [ 0.9161113   0.33408794 -0.5336739 ]\n [ 0.28828338  0.25902364 -0.18665437]\n [ 1.0131412   0.5671034  -0.61120325]\n [ 1.0753375   0.6574415  -0.6641773 ]\n [ 0.44239897  0.41021895 -0.2870154 ]\n [ 0.38235447  0.13146614 -0.21256155]\n [ 0.4230119   0.3564242  -0.27039707]\n [ 0.30869636  0.16568774 -0.17577897]\n [ 1.0061666   0.5303687  -0.61857384]], shape=(16, 3), dtype=float32)\n"
    }
   ],
   "source": [
    "print(features)\n",
    "predictions = model(features)\n",
    "print(predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Loss test: 0.015156036242842674\n"
    }
   ],
   "source": [
    "loss_object = tf.keras.losses.MeanSquaredError()\n",
    "import numpy as np\n",
    "import random\n",
    "\n",
    "#mask = np.array(labels.map_fn(lambda label: label == 1))\n",
    "\n",
    "#print(mask)\n",
    "#other_results = tf.boolean_mask(labels,mask)\n",
    "#print(other_results)\n",
    "\n",
    "def get_new_y(predictions, labels):\n",
    "  new_y = []\n",
    "  for idx, val in enumerate(predictions.numpy()):\n",
    "      label = labels[idx].numpy()\n",
    "      relevant_data = []\n",
    "      for idx2, value in enumerate(labels.numpy()):\n",
    "          if(value == label and idx2 != idx):\n",
    "              relevant_data.append(predictions[idx2])\n",
    "      new_y.append(relevant_data[random.randint(0,len(relevant_data)-1)])\n",
    "  return new_y\n",
    "\n",
    "def loss(model, x, y, training):\n",
    "  # training=training is needed only if there are layers with different\n",
    "  # behavior during training versus inference (e.g. Dropout).\n",
    "  y_ = model(x, training=training)\n",
    "  #print(x)\n",
    "  new_y = get_new_y(y_, y)\n",
    "\n",
    "  return loss_object(y_true=new_y, y_pred=y_)\n",
    "\n",
    "\n",
    "l = loss(model, features, labels, training=False)\n",
    "print(\"Loss test: {}\".format(l))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def grad(model, inputs, targets):\n",
    "  with tf.GradientTape() as tape:\n",
    "    loss_value = loss(model, inputs, targets, training=True)\n",
    "  return loss_value, tape.gradient(loss_value, model.trainable_variables)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer = tf.keras.optimizers.SGD(learning_rate=0.01)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Step: 0, Initial Loss: 0.017227251082658768\nStep: 1,         Loss: 0.016855113208293915\n"
    }
   ],
   "source": [
    "loss_value, grads = grad(model, features, labels)\n",
    "\n",
    "print(\"Step: {}, Initial Loss: {}\".format(optimizer.iterations.numpy(),\n",
    "                                          loss_value.numpy()))\n",
    "\n",
    "optimizer.apply_gradients(zip(grads, model.trainable_variables))\n",
    "\n",
    "print(\"Step: {},         Loss: {}\".format(optimizer.iterations.numpy(),\n",
    "                                          loss(model, features, labels, training=True).numpy()))"
   ]
  },
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   "cell_type": "code",
   "execution_count": 20,
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   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Epoch 000: Loss: 0.018, MAE: 0.0905\nEpoch 050: Loss: 0.017, MAE: 0.0928\nEpoch 100: Loss: 0.013, MAE: 0.0924\nEpoch 150: Loss: 0.011, MAE: 0.0968\nEpoch 200: Loss: 0.013, MAE: 0.0845\nEpoch 250: Loss: 0.011, MAE: 0.0954\nEpoch 300: Loss: 0.012, MAE: 0.0647\nEpoch 350: Loss: 0.012, MAE: 0.0665\nEpoch 400: Loss: 0.010, MAE: 0.0635\nEpoch 450: Loss: 0.010, MAE: 0.0763\nEpoch 500: Loss: 0.006, MAE: 0.0696\nEpoch 550: Loss: 0.009, MAE: 0.0685\nEpoch 600: Loss: 0.006, MAE: 0.0629\nEpoch 650: Loss: 0.007, MAE: 0.0749\nEpoch 700: Loss: 0.007, MAE: 0.063\nEpoch 750: Loss: 0.006, MAE: 0.0683\nEpoch 800: Loss: 0.006, MAE: 0.0665\nEpoch 850: Loss: 0.005, MAE: 0.0618\nEpoch 900: Loss: 0.005, MAE: 0.0553\nEpoch 950: Loss: 0.004, MAE: 0.0456\nEpoch 1000: Loss: 0.004, MAE: 0.0488\nEpoch 1050: Loss: 0.005, MAE: 0.0617\nEpoch 1100: Loss: 0.004, MAE: 0.0462\nEpoch 1150: Loss: 0.003, MAE: 0.0452\nEpoch 1200: Loss: 0.006, MAE: 0.0608\nEpoch 1250: Loss: 0.004, MAE: 0.0552\nEpoch 1300: Loss: 0.004, MAE: 0.0596\nEpoch 1350: Loss: 0.003, MAE: 0.0506\nEpoch 1400: Loss: 0.004, MAE: 0.0521\nEpoch 1450: Loss: 0.003, MAE: 0.0507\nEpoch 1500: Loss: 0.005, MAE: 0.0543\n"
    }
   ],
   "source": [
    "## Note: Rerunning this cell uses the same model variables\n",
    "\n",
    "# Keep results for plotting\n",
    "train_loss_results = []\n",
    "train_accuracy_results = []\n",
    "num_epochs = 1501\n",
    "\n",
    "for epoch in range(num_epochs):\n",
    "  epoch_loss_avg = tf.keras.metrics.Mean()\n",
    "  epoch_accuracy = tf.keras.metrics.MeanAbsoluteError()\n",
    "\n",
    "  # Training loop - using batches of 32\n",
    "  for x, y in train_dataset:\n",
    "    # Optimize the model\n",
    "    loss_value, grads = grad(model, x, y)\n",
    "    optimizer.apply_gradients(zip(grads, model.trainable_variables))\n",
    "\n",
    "    # Track progress\n",
    "    epoch_loss_avg.update_state(loss_value)  # Add current batch loss\n",
    "    # Compare predicted label to actual label\n",
    "    # training=True is needed only if there are layers with different\n",
    "    # behavior during training versus inference (e.g. Dropout).\n",
    "    predictions = model(x, training=True)\n",
    "    new_y = get_new_y(predictions, y)\n",
    "    epoch_accuracy.update_state(new_y, predictions)\n",
    "\n",
    "  # End epoch\n",
    "  train_loss_results.append(epoch_loss_avg.result())\n",
    "  train_accuracy_results.append(epoch_accuracy.result())\n",
    "\n",
    "  if epoch % 50 == 0:\n",
    "    print(\"Epoch {:03d}: Loss: {:.3f}, MAE: {:.3}\".format(epoch,\n",
    "                                                                epoch_loss_avg.result(),\n",
    "                                                                epoch_accuracy.result()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "[[ 0.28591654  0.20274727 -0.19525181]\n [ 0.7021659   0.4857812  -0.40036255]\n [ 0.22559318  0.2053052  -0.14340849]\n [ 0.5975208   0.46692458 -0.31813243]]\n0.07958128\n0.1344171\n0.42659882\n0.6097653\n"
    }
   ],
   "source": [
    " a = [0.177, 0.24, 0.512, 0.423]\n",
    " b = [0.9277, 0.614, 0.5612, 0.9323]\n",
    " c = [0.142, 0.423, 0.223, 0.312]\n",
    " d = [0.846, 0.947, 0.542, 0.657]\n",
    " results = model(tf.constant([a,b,c,d]), training=False).numpy()\n",
    " print(results)\n",
    " print(np.linalg.norm(results[0]-results[2]))\n",
    " print(np.linalg.norm(results[1]-results[3]))\n",
    "\n",
    " print(np.linalg.norm(results[0]-results[3]))\n",
    " print(np.linalg.norm(results[1]-results[2]))"
   ]
  }
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