scratch-triplet-loss-person-reid.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn.metrics import roc_curve\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "import random\n",
    "import matplotlib.patheffects as PathEffects\n",
    "from tensorflow.keras.datasets import cifar10 as cifar10\n",
    "\n",
    "## for Model definition/training\n",
    "from tensorflow.keras.models import Model, load_model, Sequential\n",
    "from tensorflow.keras.layers import Input, Flatten, Dense, concatenate,  Dropout, Conv2D, BatchNormalization, MaxPool2D, Activation, Dense\n",
    "from tensorflow.keras.optimizers import Adam, Nadam, Adadelta,SGD\n",
    "from tensorflow.keras import regularizers\n",
    "\n",
    "from tensorflow.keras.utils import plot_model\n",
    "from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import pairwise_distances\n",
    "from sklearn.manifold import TSNE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import defaultdict\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "base_dir = 'data/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total id1: 319\n",
      "total id2 : 325\n",
      "total id3 : 330\n",
      "total id4 : 318\n",
      "total id5 : 330\n",
      "total id6 : 330\n",
      "total id7 : 321\n",
      "total id8 : 284\n",
      "2557\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "\n",
    "id1= len(os.listdir(base_dir+'/0'))\n",
    "id2 = len(os.listdir(base_dir+'/1'))\n",
    "id3 = len(os.listdir(base_dir+'/2'))\n",
    "id4 = len(os.listdir(base_dir+'/3'))\n",
    "id5 = len(os.listdir(base_dir+'/4'))\n",
    "id6 = len(os.listdir(base_dir+'/5'))\n",
    "id7=len(os.listdir(base_dir+'/6'))\n",
    "id8=len(os.listdir(base_dir+'/7'))\n",
    "\n",
    "print('total id1:', id1)\n",
    "print('total id2 :',id2)\n",
    "print('total id3 :', id3)\n",
    "print('total id4 :', id4)\n",
    "print('total id5 :', id5)\n",
    "print('total id6 :', id6)\n",
    "print('total id7 :', id7)\n",
    "print('total id8 :', id8)\n",
    "\n",
    "total_data = id1+id2+id3+id4+id5+id6+id7+id8\n",
    "print(total_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pairwise_distance(feature, squared=False):\n",
    "    \"\"\"Computes the pairwise distance matrix with numerical stability.\n",
    "\n",
    "    output[i, j] = || feature[i, :] - feature[j, :] ||_2\n",
    "\n",
    "    Args:\n",
    "      feature: 2-D Tensor of size [number of data, feature dimension].\n",
    "      squared: Boolean, whether or not to square the pairwise distances.\n",
    "\n",
    "    Returns:\n",
    "      pairwise_distances: 2-D Tensor of size [number of data, number of data].\n",
    "    \"\"\"\n",
    "    pairwise_distances_squared = math_ops.add(\n",
    "        math_ops.reduce_sum(math_ops.square(feature), axis=[1], keepdims=True),\n",
    "        math_ops.reduce_sum(\n",
    "            math_ops.square(array_ops.transpose(feature)),\n",
    "            axis=[0],\n",
    "            keepdims=True)) - 2.0 * math_ops.matmul(feature,\n",
    "                                                    array_ops.transpose(feature))\n",
    "\n",
    "    # Deal with numerical inaccuracies. Set small negatives to zero.\n",
    "    pairwise_distances_squared = math_ops.maximum(pairwise_distances_squared, 0.0)\n",
    "    # Get the mask where the zero distances are at.\n",
    "    error_mask = math_ops.less_equal(pairwise_distances_squared, 0.0)\n",
    "\n",
    "    # Optionally take the sqrt.\n",
    "    if squared:\n",
    "        pairwise_distances = pairwise_distances_squared\n",
    "    else:\n",
    "        pairwise_distances = math_ops.sqrt(\n",
    "            pairwise_distances_squared + math_ops.to_float(error_mask) * 1e-16)\n",
    "\n",
    "    # Undo conditionally adding 1e-16.\n",
    "    pairwise_distances = math_ops.multiply(\n",
    "        pairwise_distances, math_ops.to_float(math_ops.logical_not(error_mask)))\n",
    "\n",
    "    num_data = array_ops.shape(feature)[0]\n",
    "    # Explicitly set diagonals to zero.\n",
    "    mask_offdiagonals = array_ops.ones_like(pairwise_distances) - array_ops.diag(\n",
    "        array_ops.ones([num_data]))\n",
    "    pairwise_distances = math_ops.multiply(pairwise_distances, mask_offdiagonals)\n",
    "    return pairwise_distances\n",
    "\n",
    "def masked_maximum(data, mask, dim=1):\n",
    "    \"\"\"Computes the axis wise maximum over chosen elements.\n",
    "\n",
    "    Args:\n",
    "      data: 2-D float `Tensor` of size [n, m].\n",
    "      mask: 2-D Boolean `Tensor` of size [n, m].\n",
    "      dim: The dimension over which to compute the maximum.\n",
    "\n",
    "    Returns:\n",
    "      masked_maximums: N-D `Tensor`.\n",
    "        The maximized dimension is of size 1 after the operation.\n",
    "    \"\"\"\n",
    "    axis_minimums = math_ops.reduce_min(data, dim, keepdims=True)\n",
    "    masked_maximums = math_ops.reduce_max(\n",
    "        math_ops.multiply(data - axis_minimums, mask), dim,\n",
    "        keepdims=True) + axis_minimums\n",
    "    return masked_maximums\n",
    "\n",
    "def masked_minimum(data, mask, dim=1):\n",
    "    \"\"\"Computes the axis wise minimum over chosen elements.\n",
    "\n",
    "    Args:\n",
    "      data: 2-D float `Tensor` of size [n, m].\n",
    "      mask: 2-D Boolean `Tensor` of size [n, m].\n",
    "      dim: The dimension over which to compute the minimum.\n",
    "\n",
    "    Returns:\n",
    "      masked_minimums: N-D `Tensor`.\n",
    "        The minimized dimension is of size 1 after the operation.\n",
    "    \"\"\"\n",
    "    axis_maximums = math_ops.reduce_max(data, dim, keepdims=True)\n",
    "    masked_minimums = math_ops.reduce_min(\n",
    "        math_ops.multiply(data - axis_maximums, mask), dim,\n",
    "        keepdims=True) + axis_maximums\n",
    "    return masked_minimums"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.python.ops import math_ops\n",
    "from tensorflow.python.ops import array_ops\n",
    "from tensorflow import dtypes\n",
    "def triplet_loss_adapted_from_tf(y_true, y_pred):\n",
    "    del y_true\n",
    "    margin = 1.\n",
    "    labels = y_pred[:, :1]\n",
    "\n",
    " \n",
    "    labels = tf.cast(labels, dtype='int32')\n",
    "\n",
    "    embeddings = y_pred[:, 1:]\n",
    "\n",
    "    ### Code from Tensorflow function [tf.contrib.losses.metric_learning.triplet_semihard_loss] starts here:\n",
    "    \n",
    "    # Reshape [batch_size] label tensor to a [batch_size, 1] label tensor.\n",
    "    # lshape=array_ops.shape(labels)\n",
    "    # assert lshape.shape == 1\n",
    "    # labels = array_ops.reshape(labels, [lshape[0], 1])\n",
    "\n",
    "    # Build pairwise squared distance matrix.\n",
    "    pdist_matrix = pairwise_distance(embeddings, squared=True)\n",
    "    # Build pairwise binary adjacency matrix.\n",
    "    adjacency = math_ops.equal(labels, array_ops.transpose(labels))\n",
    "    # Invert so we can select negatives only.\n",
    "    adjacency_not = math_ops.logical_not(adjacency)\n",
    "\n",
    "    # global batch_size  \n",
    "    batch_size = array_ops.size(labels) # was 'array_ops.size(labels)'\n",
    "\n",
    "    # Compute the mask.\n",
    "    pdist_matrix_tile = array_ops.tile(pdist_matrix, [batch_size, 1])\n",
    "    mask = math_ops.logical_and(\n",
    "        array_ops.tile(adjacency_not, [batch_size, 1]),\n",
    "        math_ops.greater(\n",
    "            pdist_matrix_tile, array_ops.reshape(\n",
    "                array_ops.transpose(pdist_matrix), [-1, 1])))\n",
    "    mask_final = array_ops.reshape(\n",
    "        math_ops.greater(\n",
    "            math_ops.reduce_sum(\n",
    "                math_ops.cast(mask, dtype=dtypes.float32), 1, keepdims=True),\n",
    "            0.0), [batch_size, batch_size])\n",
    "    mask_final = array_ops.transpose(mask_final)\n",
    "\n",
    "    adjacency_not = math_ops.cast(adjacency_not, dtype=dtypes.float32)\n",
    "    mask = math_ops.cast(mask, dtype=dtypes.float32)\n",
    "\n",
    "    # negatives_outside: smallest D_an where D_an > D_ap.\n",
    "    negatives_outside = array_ops.reshape(\n",
    "        masked_minimum(pdist_matrix_tile, mask), [batch_size, batch_size])\n",
    "    negatives_outside = array_ops.transpose(negatives_outside)\n",
    "\n",
    "    # negatives_inside: largest D_an.\n",
    "    negatives_inside = array_ops.tile(\n",
    "        masked_maximum(pdist_matrix, adjacency_not), [1, batch_size])\n",
    "    semi_hard_negatives = array_ops.where(\n",
    "        mask_final, negatives_outside, negatives_inside)\n",
    "\n",
    "    loss_mat = math_ops.add(margin, pdist_matrix - semi_hard_negatives)\n",
    "\n",
    "    mask_positives = math_ops.cast(\n",
    "        adjacency, dtype=dtypes.float32) - array_ops.diag(\n",
    "        array_ops.ones([batch_size]))\n",
    "\n",
    "    # In lifted-struct, the authors multiply 0.5 for upper triangular\n",
    "    #   in semihard, they take all positive pairs except the diagonal.\n",
    "    num_positives = math_ops.reduce_sum(mask_positives)\n",
    "\n",
    "    semi_hard_triplet_loss_distance = math_ops.truediv(\n",
    "        math_ops.reduce_sum(\n",
    "            math_ops.maximum(\n",
    "                math_ops.multiply(loss_mat, mask_positives), 0.0)),\n",
    "        num_positives,\n",
    "        name='triplet_semihard_loss')\n",
    "    \n",
    "    ### Code from Tensorflow function semi-hard triplet loss ENDS here.\n",
    "    return semi_hard_triplet_loss_distance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# x_train_flat = x_train.flatten().reshape(-1,3072)\n",
    "# x_test_flat = x_test.flatten().reshape(-1,3072)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 2048 images belonging to 8 classes.\n",
      "Found 509 images belonging to 8 classes.\n"
     ]
    }
   ],
   "source": [
    "\n",
    "   \n",
    "\n",
    "# The data, split between train and test sets\n",
    "datagen = ImageDataGenerator(\n",
    "#             rotation_range=45,\n",
    "#             horizontal_flip=True,\n",
    "#             vertical_flip=True,\n",
    "#             rescale=1/255, \n",
    "#             zoom_range=0.2,\n",
    "            validation_split=0.2) #Dataset dibagi menjadi 75% train set dan 25% validation set.\n",
    "\n",
    "train = datagen.flow_from_directory(\n",
    "    base_dir,\n",
    "    target_size=(180, 80),\n",
    "    batch_size=2557,\n",
    "    class_mode='binary',\n",
    "    subset='training'\n",
    ")\n",
    "\n",
    "valid= datagen.flow_from_directory(\n",
    "    base_dir,\n",
    "    target_size=(180, 80),\n",
    "    batch_size=2557,\n",
    "    class_mode='binary',\n",
    "    subset='validation'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.applications.efficientnet import EfficientNetB3\n",
    "\n",
    "from tensorflow.keras.applications.xception import Xception, preprocess_input\n",
    "def create_base_network(image_input_shape, embedding_size):\n",
    "    \"\"\"\n",
    "    Base network to be shared (eq. to feature extraction).\n",
    "    \"\"\"\n",
    "    input_image = Input(shape=image_input_shape)\n",
    "    decay = 0.000001\n",
    "    x = Conv2D(16,(3,3),padding='same', activation='relu',kernel_regularizer=regularizers.l2(decay),\n",
    "                activity_regularizer=regularizers.l2(decay))(input_image)\n",
    "    x = BatchNormalization()(x)\n",
    "    x = MaxPool2D(pool_size=(2, 2))(x)\n",
    "    x = Conv2D(16,(3,3),padding='same', activation='relu',kernel_regularizer=regularizers.l2(decay),\n",
    "                activity_regularizer=regularizers.l2(decay))(x)\n",
    "    x = BatchNormalization()(x)\n",
    "    x = MaxPool2D(pool_size=(2, 2))(x)\n",
    "    x = Conv2D(32,(3,3),padding='same', activation='relu',kernel_regularizer=regularizers.l2(decay),\n",
    "                activity_regularizer=regularizers.l2(decay))(x)\n",
    "    x = BatchNormalization()(x)\n",
    "    x = MaxPool2D(pool_size=(2, 2))(x)\n",
    "    x = Conv2D(32,(3,3),padding='same', activation='relu',kernel_regularizer=regularizers.l2(decay),\n",
    "                activity_regularizer=regularizers.l2(decay))(x)\n",
    "    x = BatchNormalization()(x)\n",
    "    x = MaxPool2D(pool_size=(2, 2))(x)\n",
    "#     x = EfficientNetB3( weights='imagenet', include_top=False)(input_image)\n",
    "    x = Dropout(0.2)(x)\n",
    "    \n",
    "    x = Flatten()(x)\n",
    "    x = Dense(256, activation='relu')(x)\n",
    "    x = Dropout(0.1)(x)\n",
    "    x = Dense(embedding_size)(x)\n",
    "    \n",
    "    \n",
    "#     base_network = Model(inputs=input_image, outputs=base_model.get_layer('avg_pool').output)\n",
    "    \n",
    "    base_network = Model(inputs=input_image, outputs=x)\n",
    "#     plot_model(base_network, to_file='base_network.png', show_shapes=True, show_layer_names=True)\n",
    "    return base_network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "x_train, y_train = next(train)\n",
    "x_test, y_test = next(valid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "x_train = x_train.astype('float32')\n",
    "x_test = x_test.astype('float32')\n",
    "y_train = y_train.astype('uint8')\n",
    "y_test = y_test.astype('uint8')\n",
    "# x_train /= 255.\n",
    "# x_test /= 255.\n",
    "input_image_shape = (x_train.shape[1], x_train.shape[2], 3)\n",
    "x_val = x_test\n",
    "y_val = y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5, 7, 3, ..., 5, 7, 6], dtype=uint8)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(509, 180, 80, 3)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_val.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    " # in case this scriot is called from another file, let's make sure it doesn't start training the network...\n",
    "\n",
    "batch_size = 64\n",
    "epochs = 3000\n",
    "train_flag = True  # either     True or False\n",
    "\n",
    "embedding_size = 128\n",
    "\n",
    "no_of_components = 2  # for visualization -> PCA.fit_transform()\n",
    "\n",
    "step = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"functional_54\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_image (InputLayer)        [(None, 180, 80, 3)] 0                                            \n",
      "__________________________________________________________________________________________________\n",
      "input_label (InputLayer)        [(None, 1)]          0                                            \n",
      "__________________________________________________________________________________________________\n",
      "functional_52 (Functional)      (None, 8)            469912      input_image[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_12 (Concatenate)    (None, 9)            0           input_label[0][0]                \n",
      "                                                                 functional_52[0][0]              \n",
      "==================================================================================================\n",
      "Total params: 469,912\n",
      "Trainable params: 469,720\n",
      "Non-trainable params: 192\n",
      "__________________________________________________________________________________________________\n",
      "Epoch 1/3000\n"
     ]
    },
    {
     "ename": "AssertionError",
     "evalue": "in user code:\n\n    C:\\Users\\farha\\anaconda3\\envs\\yolov4-gpu\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\training.py:806 train_function  *\n        return step_function(self, iterator)\n    C:\\Users\\farha\\anaconda3\\envs\\yolov4-gpu\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\training.py:796 step_function  **\n        outputs = model.distribute_strategy.run(run_step, args=(data,))\n    C:\\Users\\farha\\anaconda3\\envs\\yolov4-gpu\\lib\\site-packages\\tensorflow\\python\\distribute\\distribute_lib.py:1211 run\n        return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)\n    C:\\Users\\farha\\anaconda3\\envs\\yolov4-gpu\\lib\\site-packages\\tensorflow\\python\\distribute\\distribute_lib.py:2585 call_for_each_replica\n        return self._call_for_each_replica(fn, args, kwargs)\n    C:\\Users\\farha\\anaconda3\\envs\\yolov4-gpu\\lib\\site-packages\\tensorflow\\python\\distribute\\distribute_lib.py:2945 _call_for_each_replica\n        return fn(*args, **kwargs)\n    C:\\Users\\farha\\anaconda3\\envs\\yolov4-gpu\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\training.py:789 run_step  **\n        outputs = model.train_step(data)\n    C:\\Users\\farha\\anaconda3\\envs\\yolov4-gpu\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\training.py:747 train_step\n        y_pred = self(x, training=True)\n    C:\\Users\\farha\\anaconda3\\envs\\yolov4-gpu\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\base_layer.py:985 __call__\n        outputs = call_fn(inputs, *args, **kwargs)\n    C:\\Users\\farha\\anaconda3\\envs\\yolov4-gpu\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\functional.py:386 call\n        inputs, training=training, mask=mask)\n    C:\\Users\\farha\\anaconda3\\envs\\yolov4-gpu\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\functional.py:517 _run_internal_graph\n        assert x_id in tensor_dict, 'Could not compute output ' + str(x)\n\n    AssertionError: Could not compute output Tensor(\"concatenate_12/concat:0\", shape=(None, 9), dtype=float32)\n",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-44-994e4280d42c>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     46\u001b[0m     \u001b[0mx_val\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx_val\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx_val\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx_train\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx_train\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m3\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     47\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 48\u001b[1;33m     \u001b[0mH\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mepochs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalidation_data\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx_val\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_val\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     49\u001b[0m \u001b[1;31m#     H = model.fit(\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     50\u001b[0m \u001b[1;31m#         x=[x_train,y_train],\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\yolov4-gpu\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\training.py\u001b[0m in \u001b[0;36m_method_wrapper\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    106\u001b[0m   \u001b[1;32mdef\u001b[0m \u001b[0m_method_wrapper\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[0;32m    107\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_in_multi_worker_mode\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m  \u001b[1;31m# pylint: disable=protected-access\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 108\u001b[1;33m       \u001b[1;32mreturn\u001b[0m \u001b[0mmethod\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[0m\n\u001b[0m\u001b[0;32m    109\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    110\u001b[0m     \u001b[1;31m# Running inside `run_distribute_coordinator` already.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\yolov4-gpu\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\training.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[0;32m   1096\u001b[0m                 batch_size=batch_size):\n\u001b[0;32m   1097\u001b[0m               \u001b[0mcallbacks\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mon_train_batch_begin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1098\u001b[1;33m               \u001b[0mtmp_logs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtrain_function\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1099\u001b[0m               \u001b[1;32mif\u001b[0m \u001b[0mdata_handler\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshould_sync\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1100\u001b[0m                 \u001b[0mcontext\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masync_wait\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\yolov4-gpu\\lib\\site-packages\\tensorflow\\python\\eager\\def_function.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwds)\u001b[0m\n\u001b[0;32m    778\u001b[0m       \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    779\u001b[0m         \u001b[0mcompiler\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"nonXla\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 780\u001b[1;33m         \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_call\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[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    781\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    782\u001b[0m       \u001b[0mnew_tracing_count\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_tracing_count\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\yolov4-gpu\\lib\\site-packages\\tensorflow\\python\\eager\\def_function.py\u001b[0m in \u001b[0;36m_call\u001b[1;34m(self, *args, **kwds)\u001b[0m\n\u001b[0;32m    821\u001b[0m       \u001b[1;31m# This is the first call of __call__, so we have to initialize.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    822\u001b[0m       \u001b[0minitializers\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--> 823\u001b[1;33m       \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_initialize\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0madd_initializers_to\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0minitializers\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    824\u001b[0m     \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    825\u001b[0m       \u001b[1;31m# At this point we know that the initialization is complete (or less\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\yolov4-gpu\\lib\\site-packages\\tensorflow\\python\\eager\\def_function.py\u001b[0m in \u001b[0;36m_initialize\u001b[1;34m(self, args, kwds, add_initializers_to)\u001b[0m\n\u001b[0;32m    695\u001b[0m     self._concrete_stateful_fn = (\n\u001b[0;32m    696\u001b[0m         self._stateful_fn._get_concrete_function_internal_garbage_collected(  # pylint: disable=protected-access\n\u001b[1;32m--> 697\u001b[1;33m             *args, **kwds))\n\u001b[0m\u001b[0;32m    698\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    699\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0minvalid_creator_scope\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0munused_args\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0munused_kwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\yolov4-gpu\\lib\\site-packages\\tensorflow\\python\\eager\\function.py\u001b[0m in \u001b[0;36m_get_concrete_function_internal_garbage_collected\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   2853\u001b[0m       \u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2854\u001b[0m     \u001b[1;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2855\u001b[1;33m       \u001b[0mgraph_function\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_maybe_define_function\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0margs\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   2856\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mgraph_function\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2857\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\yolov4-gpu\\lib\\site-packages\\tensorflow\\python\\eager\\function.py\u001b[0m in \u001b[0;36m_maybe_define_function\u001b[1;34m(self, args, kwargs)\u001b[0m\n\u001b[0;32m   3211\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3212\u001b[0m       \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_function_cache\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmissed\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcall_context_key\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3213\u001b[1;33m       \u001b[0mgraph_function\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_create_graph_function\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0margs\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   3214\u001b[0m       \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_function_cache\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprimary\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcache_key\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgraph_function\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3215\u001b[0m       \u001b[1;32mreturn\u001b[0m \u001b[0mgraph_function\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\yolov4-gpu\\lib\\site-packages\\tensorflow\\python\\eager\\function.py\u001b[0m in \u001b[0;36m_create_graph_function\u001b[1;34m(self, args, kwargs, override_flat_arg_shapes)\u001b[0m\n\u001b[0;32m   3073\u001b[0m             \u001b[0marg_names\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0marg_names\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3074\u001b[0m             \u001b[0moverride_flat_arg_shapes\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0moverride_flat_arg_shapes\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3075\u001b[1;33m             capture_by_value=self._capture_by_value),\n\u001b[0m\u001b[0;32m   3076\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_function_attributes\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3077\u001b[0m         \u001b[0mfunction_spec\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfunction_spec\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\yolov4-gpu\\lib\\site-packages\\tensorflow\\python\\framework\\func_graph.py\u001b[0m in \u001b[0;36mfunc_graph_from_py_func\u001b[1;34m(name, python_func, args, kwargs, signature, func_graph, autograph, autograph_options, add_control_dependencies, arg_names, op_return_value, collections, capture_by_value, override_flat_arg_shapes)\u001b[0m\n\u001b[0;32m    984\u001b[0m         \u001b[0m_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moriginal_func\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtf_decorator\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munwrap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpython_func\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    985\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 986\u001b[1;33m       \u001b[0mfunc_outputs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpython_func\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mfunc_args\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mfunc_kwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    987\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    988\u001b[0m       \u001b[1;31m# invariant: `func_outputs` contains only Tensors, CompositeTensors,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\yolov4-gpu\\lib\\site-packages\\tensorflow\\python\\eager\\def_function.py\u001b[0m in \u001b[0;36mwrapped_fn\u001b[1;34m(*args, **kwds)\u001b[0m\n\u001b[0;32m    598\u001b[0m         \u001b[1;31m# __wrapped__ allows AutoGraph to swap in a converted function. We give\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    599\u001b[0m         \u001b[1;31m# the function a weak reference to itself to avoid a reference cycle.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 600\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mweak_wrapped_fn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__wrapped__\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[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    601\u001b[0m     \u001b[0mweak_wrapped_fn\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mweakref\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mref\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mwrapped_fn\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    602\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\yolov4-gpu\\lib\\site-packages\\tensorflow\\python\\framework\\func_graph.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    971\u001b[0m           \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m  \u001b[1;31m# pylint:disable=broad-except\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    972\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"ag_error_metadata\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 973\u001b[1;33m               \u001b[1;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mag_error_metadata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_exception\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    974\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    975\u001b[0m               \u001b[1;32mraise\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAssertionError\u001b[0m: in user code:\n\n    C:\\Users\\farha\\anaconda3\\envs\\yolov4-gpu\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\training.py:806 train_function  *\n        return step_function(self, iterator)\n    C:\\Users\\farha\\anaconda3\\envs\\yolov4-gpu\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\training.py:796 step_function  **\n        outputs = model.distribute_strategy.run(run_step, args=(data,))\n    C:\\Users\\farha\\anaconda3\\envs\\yolov4-gpu\\lib\\site-packages\\tensorflow\\python\\distribute\\distribute_lib.py:1211 run\n        return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)\n    C:\\Users\\farha\\anaconda3\\envs\\yolov4-gpu\\lib\\site-packages\\tensorflow\\python\\distribute\\distribute_lib.py:2585 call_for_each_replica\n        return self._call_for_each_replica(fn, args, kwargs)\n    C:\\Users\\farha\\anaconda3\\envs\\yolov4-gpu\\lib\\site-packages\\tensorflow\\python\\distribute\\distribute_lib.py:2945 _call_for_each_replica\n        return fn(*args, **kwargs)\n    C:\\Users\\farha\\anaconda3\\envs\\yolov4-gpu\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\training.py:789 run_step  **\n        outputs = model.train_step(data)\n    C:\\Users\\farha\\anaconda3\\envs\\yolov4-gpu\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\training.py:747 train_step\n        y_pred = self(x, training=True)\n    C:\\Users\\farha\\anaconda3\\envs\\yolov4-gpu\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\base_layer.py:985 __call__\n        outputs = call_fn(inputs, *args, **kwargs)\n    C:\\Users\\farha\\anaconda3\\envs\\yolov4-gpu\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\functional.py:386 call\n        inputs, training=training, mask=mask)\n    C:\\Users\\farha\\anaconda3\\envs\\yolov4-gpu\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\functional.py:517 _run_internal_graph\n        assert x_id in tensor_dict, 'Could not compute output ' + str(x)\n\n    AssertionError: Could not compute output Tensor(\"concatenate_12/concat:0\", shape=(None, 9), dtype=float32)\n"
     ]
    }
   ],
   "source": [
    "# Network training...\n",
    "base_network = create_base_network(input_image_shape, embedding_size)\n",
    "    \n",
    "if train_flag == True:\n",
    "    filepath = 'best_model_triplet.hdf5'\n",
    "\n",
    "    checkpoint = ModelCheckpoint(filepath=filepath,\n",
    "                                 monitor='val_loss',\n",
    "                                 verbose=1,\n",
    "                                 save_best_only=True)\n",
    "    \n",
    "    stop = EarlyStopping(monitor='val_loss', patience =10,\n",
    "                          verbose=0, mode='auto', baseline=None, \n",
    "                      restore_best_weights=False)\n",
    "    \n",
    "    callbacks = [checkpoint,stop]\n",
    "    \n",
    "    input_images = Input(shape=input_image_shape, name='input_image') # input layer for images\n",
    "    input_labels = Input(shape=(1,), name='input_label')    # input layer for labels\n",
    "    embeddings = base_network([input_images])   \n",
    "    # output of network -> embeddings\n",
    "    labels_plus_embeddings = concatenate([input_labels, embeddings])  # concatenating the labels + embeddings\n",
    "\n",
    "    # Defining a model with inputs (images, labels) and outputs (labels_plus_embeddings)\n",
    "    model = Model(inputs=[input_images, input_labels],\n",
    "                  outputs=labels_plus_embeddings)\n",
    "\n",
    "    model.summary()\n",
    "    plot_model(model, to_file='model.png', show_shapes=True, show_layer_names=True)\n",
    "\n",
    "    # train session\n",
    "    opt = Adam(lr=0.001)  # choose optimiser. RMS is good too!\n",
    "\n",
    "    model.compile(loss=triplet_loss_adapted_from_tf,\n",
    "                  optimizer=opt)\n",
    "\n",
    "#     filepath = \"semiH_trip_MNIST_v13_ep{epoch:02d}_BS%d.hdf5\" % batch_size\n",
    "#     checkpoint = ModelCheckpoint(filepath, monitor='val_loss', verbose=1, save_best_only=False, period=25)\n",
    "\n",
    "    # Uses 'dummy' embeddings + dummy gt labels. Will be removed as soon as loaded, to free memory\n",
    "    dummy_gt_train = np.zeros((len(x_train), embedding_size + 1))\n",
    "    dummy_gt_val = np.zeros((len(x_val), embedding_size + 1))\n",
    "\n",
    "    x_train = np.reshape(x_train, (len(x_train), x_train.shape[1], x_train.shape[2], 3))\n",
    "    x_val = np.reshape(x_val, (len(x_val), x_train.shape[1], x_train.shape[2], 3))\n",
    "\n",
    "    H = model.fit(\n",
    "        x=[x_train,y_train],\n",
    "        y=dummy_gt_train,\n",
    "        batch_size=batch_size,\n",
    "        epochs=epochs,\n",
    "        validation_data=([x_val, y_val], dummy_gt_val),\n",
    "        callbacks=callbacks)\n",
    "\n",
    "    plt.figure(figsize=(8,8))\n",
    "    plt.plot(H.history['loss'], label='training loss')\n",
    "    plt.plot(H.history['val_loss'], label='validation loss')\n",
    "    plt.legend()\n",
    "    plt.title('Train/validation loss')\n",
    "    plt.show() \n",
    "else:\n",
    "\n",
    "    #####\n",
    "    model = load_model('semiH_trip_MNIST_v13_ep25_BS256.hdf5',\n",
    "                                    custom_objects={'triplet_loss_adapted_from_tf':triplet_loss_adapted_from_tf})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_model(model, to_file='model_plot.png', show_shapes=True, show_layer_names=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Anchor = X_train[:,0,:].reshape(-1,28,28,1)\n",
    "Positive = X_train[:,1,:].reshape(-1,28,28,1)\n",
    "Negative = X_train[:,2,:].reshape(-1,28,28,1)\n",
    "Anchor_test = X_test[:,0,:].reshape(-1,28,28,1)\n",
    "Positive_test = X_test[:,1,:].reshape(-1,28,28,1)\n",
    "Negative_test = X_test[:,2,:].reshape(-1,28,28,1)\n",
    "\n",
    "Y_dummy = np.empty((Anchor.shape[0],300))\n",
    "Y_dummy2 = np.empty((Anchor_test.shape[0],1))\n",
    "\n",
    "model.fit([Anchor,Positive,Negative],y=Y_dummy,validation_data=([Anchor_test,Positive_test,Negative_test],Y_dummy2), batch_size=512, epochs=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = load_model('best_model_triplet.hdf5',\n",
    "                   custom_objects={'triplet_loss_adapted_from_tf':triplet_loss_adapted_from_tf})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Test the network\n",
    "\n",
    "# creating an empty network\n",
    "testing_embeddings = create_base_network(input_image_shape,\n",
    "                                     embedding_size=embedding_size)\n",
    "x_embeddings_before_train = testing_embeddings.predict(np.reshape(x_test, (len(x_test), x_train.shape[1], x_train.shape[2],3)))\n",
    "# Grabbing the weights from the trained network\n",
    "for layer_target, layer_source in zip(testing_embeddings.layers, model.layers[2].layers):\n",
    "    weights = layer_source.get_weights()\n",
    "    layer_target.set_weights(weights)\n",
    "    del weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<tensorflow.python.keras.engine.input_layer.InputLayer at 0x29711358e10>,\n",
       " <tensorflow.python.keras.engine.input_layer.InputLayer at 0x29711354048>,\n",
       " <tensorflow.python.keras.engine.functional.Functional at 0x297096bd5f8>,\n",
       " <tensorflow.python.keras.layers.merge.Concatenate at 0x29711356048>]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.layers[:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Visualizing the effect of embeddings -> using PCA!\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "x_embeddings = testing_embeddings.predict(np.reshape(x_test, (len(x_test), x_train.shape[1], x_train.shape[2],3)))\n",
    "dict_embeddings = {}\n",
    "dict_gray = {}\n",
    "test_class_labels = np.unique(np.array(y_test))\n",
    "\n",
    "pca = PCA(n_components=no_of_components)\n",
    "decomposed_embeddings = pca.fit_transform(x_embeddings)\n",
    "#     x_test_reshaped = np.reshape(x_test, (len(x_test), 28 * 28))\n",
    "decomposed_gray = pca.fit_transform(x_embeddings_before_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\farha\\anaconda3\\envs\\yolov4-gpu\\lib\\site-packages\\ipykernel_launcher.py:6: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "  \n",
      "C:\\Users\\farha\\anaconda3\\envs\\yolov4-gpu\\lib\\site-packages\\ipykernel_launcher.py:11: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "  # This is added back by InteractiveShellApp.init_path()\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fig = plt.figure(figsize=(16, 8))\n",
    "for label in test_class_labels:\n",
    "    decomposed_embeddings_class = decomposed_embeddings[y_test == label]\n",
    "    decomposed_gray_class = decomposed_gray[y_test == label]\n",
    "\n",
    "    plt.subplot(1,2,1)\n",
    "    plt.scatter(decomposed_gray_class[::step,1], decomposed_gray_class[::step,0],label=str(label))\n",
    "    plt.title('before training (embeddings)')\n",
    "    plt.legend()\n",
    "\n",
    "    plt.subplot(1,2,2)\n",
    "    plt.scatter(decomposed_embeddings_class[::step, 1], decomposed_embeddings_class[::step, 0], label=str(label))\n",
    "    plt.title('after @%d epochs' % epochs)\n",
    "    plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\farha\\anaconda3\\envs\\yolov4-gpu\\lib\\site-packages\\ipykernel_launcher.py:11: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "  # This is added back by InteractiveShellApp.init_path()\n",
      "C:\\Users\\farha\\anaconda3\\envs\\yolov4-gpu\\lib\\site-packages\\ipykernel_launcher.py:16: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "  app.launch_new_instance()\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pca = TSNE(n_components=no_of_components)\n",
    "decomposed_embeddings = pca.fit_transform(x_embeddings)\n",
    "#     x_test_reshaped = np.reshape(x_test, (len(x_test), 28 * 28))\n",
    "decomposed_gray = pca.fit_transform(x_embeddings_before_train)\n",
    "\n",
    "fig = plt.figure(figsize=(16, 8))\n",
    "for label in test_class_labels:\n",
    "    decomposed_embeddings_class = decomposed_embeddings[y_test == label]\n",
    "    decomposed_gray_class = decomposed_gray[y_test == label]\n",
    "\n",
    "    plt.subplot(1,2,1)\n",
    "    plt.scatter(decomposed_gray_class[::step,1], decomposed_gray_class[::step,0],label=str(label))\n",
    "    plt.title('before training (embeddings)')\n",
    "    plt.legend()\n",
    "\n",
    "    plt.subplot(1,2,2)\n",
    "    plt.scatter(decomposed_embeddings_class[::step, 1], decomposed_embeddings_class[::step, 0], label=str(label))\n",
    "    plt.title('after @%d epochs' % epochs)\n",
    "    plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(509,)\n",
      "validation accuracy :  0.9449901768172888\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import accuracy_score\n",
    "x_embeddings = testing_embeddings.predict(x_train)\n",
    "y_embeddings = testing_embeddings.predict(x_val)\n",
    "\n",
    "svc = SVC(C=10, gamma=0.01,probability=True)\n",
    "svc.fit(x_embeddings, y_train)\n",
    "valid_prediction = svc.predict(y_embeddings)\n",
    "print(valid_prediction.shape)\n",
    "print(\"validation accuracy : \", accuracy_score(y_val, valid_prediction))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(509, 180, 80, 3)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = load_model('best_model_triplet.hdf5',\n",
    "                   custom_objects={'triplet_loss_adapted_from_tf':triplet_loss_adapted_from_tf})\n",
    "embedding_size=128\n",
    "input_image_shape = (180, 80, 3)\n",
    "testing_embeddings = create_base_network(input_image_shape,\n",
    "                                     embedding_size=embedding_size)\n",
    "for layer_target, layer_source in zip(testing_embeddings.layers, model.layers[2].layers):\n",
    "    weights = layer_source.get_weights()\n",
    "    layer_target.set_weights(weights)\n",
    "    del weights\n",
    "# x_embeddings = testing_embeddings.predict(x_test)\n",
    "x_embeddings = testing_embeddings.predict(np.reshape(x_test, (len(x_test), x_train.shape[1], x_train.shape[2],3)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.preprocessing import image\n",
    "import tensorflow_addons as tfa\n",
    "from tensorflow.keras.models import load_model\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "img_path = 'frame_42.png'\n",
    "le = LabelEncoder()\n",
    "labels = le.fit_transform(y_train)\n",
    "\n",
    "img = image.load_img(img_path, target_size=(180, 80))\n",
    "img_data = image.img_to_array(img)\n",
    "img_data = np.expand_dims(img_data, axis=0)\n",
    "\n",
    "img_embeddings = testing_embeddings.predict(img_data)\n",
    "preds = svc.predict_proba(img_embeddings)[0]\n",
    "j = np.argmax(preds)\n",
    "proba = preds[j]\n",
    "name = le.classes_[j]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 128)"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img_embeddings.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.02181924, 0.04891745, 0.00520145, 0.89591834, 0.00880372,\n",
       "       0.00460304, 0.00881426, 0.0059225 ])"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}