GAN MNIST tf classification (with DSIFT).ipynb

{"cells":[{"cell_type":"code","source":["from google.colab import drive\n","drive.mount('/content/drive')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"R87TiToDDoYz","executionInfo":{"status":"ok","timestamp":1669828207133,"user_tz":-330,"elapsed":26568,"user":{"displayName":"ROHAN DUBEY","userId":"11200230061670192260"}},"outputId":"08a53bf9-1574-4044-fa50-ba9d19f067c6"},"execution_count":1,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}]},{"cell_type":"code","execution_count":2,"metadata":{"id":"icQNarJ2L3CI","executionInfo":{"status":"ok","timestamp":1669828213161,"user_tz":-330,"elapsed":6033,"user":{"displayName":"ROHAN DUBEY","userId":"11200230061670192260"}}},"outputs":[],"source":["%matplotlib inline\n","from matplotlib import pyplot as plt\n","import numpy as np\n","import tensorflow as tf\n","from tensorflow import keras\n","from tensorflow.keras import Sequential, layers\n","from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout, Activation, Flatten, BatchNormalization, Input ,concatenate\n","from tensorflow.keras.utils import to_categorical\n","from tensorflow.keras.regularizers import l2\n","from tensorflow.keras.models import Model\n","from tensorflow.keras.optimizers import Adam\n","from tensorflow.keras.preprocessing.image import ImageDataGenerator\n","from tensorflow.keras.callbacks import  EarlyStopping, ModelCheckpoint\n","import glob\n","import os\n","import PIL\n","import time\n","\n","from IPython import display"]},{"cell_type":"code","execution_count":3,"metadata":{"id":"BOKI_3ROOytg","executionInfo":{"status":"ok","timestamp":1669828213163,"user_tz":-330,"elapsed":16,"user":{"displayName":"ROHAN DUBEY","userId":"11200230061670192260"}}},"outputs":[],"source":["def make_generator_model():\n","    model = tf.keras.Sequential()\n","    model.add(layers.Dense(7*7*256, use_bias=False, input_shape=(100,)))\n","    model.add(layers.BatchNormalization())\n","    model.add(layers.LeakyReLU())\n","\n","    model.add(layers.Reshape((7, 7, 256)))\n","    assert model.output_shape == (None, 7, 7, 256) # Note: None is the batch size\n","\n","    model.add(layers.Conv2DTranspose(128, (5, 5), strides=(1, 1), padding='same', use_bias=False))\n","    assert model.output_shape == (None, 7, 7, 128)\n","    model.add(layers.BatchNormalization())\n","    model.add(layers.LeakyReLU())\n","\n","    model.add(layers.Conv2DTranspose(64, (5, 5), strides=(2, 2), padding='same', use_bias=False))\n","    assert model.output_shape == (None, 14, 14, 64)\n","    model.add(layers.BatchNormalization())\n","    model.add(layers.LeakyReLU())\n","\n","    model.add(layers.Conv2DTranspose(1, (5, 5), strides=(2, 2), padding='same', use_bias=False, activation='tanh'))\n","    assert model.output_shape == (None, 28, 28, 1)\n","\n","    return model"]},{"cell_type":"code","execution_count":4,"metadata":{"id":"Ryv6SYPgKFvV","executionInfo":{"status":"ok","timestamp":1669828213163,"user_tz":-330,"elapsed":15,"user":{"displayName":"ROHAN DUBEY","userId":"11200230061670192260"}}},"outputs":[],"source":["def make_discriminator_model():\n","    model = tf.keras.Sequential()\n","    model.add(layers.Conv2D(64, (5, 5), strides=(2, 2), padding='same',\n","                                     input_shape=[28, 28, 1]))\n","    model.add(layers.LeakyReLU())\n","    model.add(layers.Dropout(0.3))\n","\n","    model.add(layers.Conv2D(128, (5, 5), strides=(2, 2), padding='same'))\n","    model.add(layers.LeakyReLU())\n","    model.add(layers.Dropout(0.3))\n","\n","    model.add(layers.Flatten())\n","    model.add(layers.Dense(1))\n","\n","    return model"]},{"cell_type":"code","source":["generator = make_generator_model()\n","discriminator = make_discriminator_model()\n","generator_optimizer = tf.keras.optimizers.Adam(1e-4)\n","discriminator_optimizer = tf.keras.optimizers.Adam(1e-4)"],"metadata":{"id":"XcHxjUjxn3oQ","executionInfo":{"status":"ok","timestamp":1669828213165,"user_tz":-330,"elapsed":17,"user":{"displayName":"ROHAN DUBEY","userId":"11200230061670192260"}}},"execution_count":5,"outputs":[]},{"cell_type":"code","execution_count":6,"metadata":{"id":"dfVvU4PWMbPP","executionInfo":{"status":"ok","timestamp":1669828213165,"user_tz":-330,"elapsed":16,"user":{"displayName":"ROHAN DUBEY","userId":"11200230061670192260"}}},"outputs":[],"source":["checkpoint_dir = '/content/drive/MyDrive/Colab Notebooks/SIFT+GAN handwriting/training_checkpoints_old'\n","checkpoint_prefix = os.path.join(checkpoint_dir, \"ckpt\")\n","checkpoint = tf.train.Checkpoint(generator_optimizer=generator_optimizer,\n","                                 discriminator_optimizer=discriminator_optimizer,\n","                                 generator=generator,\n","                                 discriminator=discriminator)"]},{"cell_type":"code","source":["checkpoint.restore(tf.train.latest_checkpoint(checkpoint_dir))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Xb4OZi6zD9UQ","executionInfo":{"status":"ok","timestamp":1669828218246,"user_tz":-330,"elapsed":5096,"user":{"displayName":"ROHAN DUBEY","userId":"11200230061670192260"}},"outputId":"e337d1cb-f131-4a91-ee42-bedfdd12c073"},"execution_count":7,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<tensorflow.python.training.tracking.util.CheckpointLoadStatus at 0x7f9b1144ee90>"]},"metadata":{},"execution_count":7}]},{"cell_type":"code","source":["noi = tf.random.normal([1, 100])\n","sample = checkpoint.generator(noi, training=False)\n","plt.imshow(sample[0, :, :, 0], cmap='gray')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":283},"id":"dPEQYWZwEALt","executionInfo":{"status":"ok","timestamp":1669828219984,"user_tz":-330,"elapsed":1742,"user":{"displayName":"ROHAN DUBEY","userId":"11200230061670192260"}},"outputId":"824c5183-7e29-4812-ee87-04540fc1d2ef"},"execution_count":8,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.image.AxesImage at 0x7f9b15e85a50>"]},"metadata":{},"execution_count":8},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 Axes>"],"image/png":"iVBORw0KGgoAAAANSUhEUgAAAPsAAAD4CAYAAAAq5pAIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAP2ElEQVR4nO3df4xV9ZnH8c/DKGpAEyyRIAUFRQzZqEVCNllj1GYbwT8UY0wncf0Bin9ULUkTFzQGYrKJ2WxpaiKYcdXStUtjrBY0mi2aJtSgjSMiILgLErCMAwgGOyPoMMyzf8zBjDjne4b769yZ5/1KJvfe89xzz8NlPnPuvd97ztfcXQBGvlFlNwCgMQg7EARhB4Ig7EAQhB0I4oxGbszM3Mxy64wMANVz90FDVlXYzexGSb+W1CLpP939iYL766yzzsqt9/T0JLfX19dXQZexpf64DqVez+e8aNtFRo1KvzA9ceJEVY9fzbab8Xe14pfxZtYi6SlJcyXNlNRqZjNr1RiA2qrmPfscSbvcfbe790j6vaSba9MWgFqrJuyTJP1twO192bLvMLNFZtZuZu28JwfKU/cP6Ny9TVKbJI0aNYq0AyWpZs/eIWnygNs/zJYBaELVhP09SdPNbKqZjZb0U0nratMWgFqr+GW8u/ea2QOS/kf9Q2/PuftHBevo+PHjufVmHK4Y7pr5c5Jqe6vn0FqR4fi7ao38ZTAzb2lpya2X+Z8XVdFYdzP/scDg8r5Uw9dlgSAIOxAEYQeCIOxAEIQdCIKwA0E0fOitYRsDgmLoDQiOsANBEHYgCMIOBEHYgSAIOxBEQ08ljfpIHbmWOpuvJPX29lZVx/DBnh0IgrADQRB2IAjCDgRB2IEgCDsQBGEHgmCcfQSYMWNGbu36669Prrtnz55kfdOmTcl60SHShw8fzq1xNuHGYs8OBEHYgSAIOxAEYQeCIOxAEIQdCIKwA0Ewzt4ERo1K/82dNWtWsv7SSy/l1saOHZtc99ixY8l6V1dXsj5+/Phk/bPPPsutPfPMM8l1V65cmawzw+zpqSrsZrZHUpekE5J63X12LZoCUHu12LNf7+6HavA4AOqI9+xAENWG3SX9yczeN7NFg93BzBaZWbuZtVe5LQBVqPZl/DXu3mFmF0hab2Yfu/uGgXdw9zZJbRJzvQFlqmrP7u4d2eVBSa9ImlOLpgDUXsVhN7MxZnbuyeuSfiJpW60aA1BbFU/ZbGbT1L83l/rfDvy3u/9bwToj8mV80Tj5zJkzk/XHHnssWZ83b16ynjo3/Ndff51cd+PGjcn6kSNHkvWbbropWR89enRurbu7O7nuzp07k/Uiqd5Sx9kPd3lTNlf8nt3dd0u6suKOADQUQ29AEIQdCIKwA0EQdiAIwg4EEeYQ16LhsdQQkSRdeOGFubV77rknue6SJUuS9aLhz6J6Z2dnbm358uXJdV977bVk/bzzzkvWv/nmm2T9hhtuyK0dP348ue6VV6YHe4qmo963b19u7ZxzzkmuOxKxZweCIOxAEIQdCIKwA0EQdiAIwg4EQdiBICo+xLWijZV4iGvROPsFF1yQrF977bW5taVLlybXnTZtWrJe9H/wyiuvJOsvvvhibq3oENavvvoqWe/t7U3Wi57X1L/NbNAjMb/1wgsvJOutra3Jesqtt96arBc9580s7xBX9uxAEIQdCIKwA0EQdiAIwg4EQdiBIAg7EESYcfYiZ555ZsX1ommLT5w4kawXna656HTQZ5yRf1qConHwoimdP//882S9nubPn5+sv/zyyxU/9q5du5L16dOnV/zYZWOcHQiOsANBEHYgCMIOBEHYgSAIOxAEYQeCCHPe+CJF5zBP1T/99NNat3NaUt+VWLFiRXLdSy65JFm/7bbbkvWi88ZXo+g7ANUo+l7Fueeem6x3dXXVsp2GKNyzm9lzZnbQzLYNWHa+ma03s53Z5bj6tgmgWkN5Gf8bSTeesmyJpLfcfbqkt7LbAJpYYdjdfYOkL05ZfLOk1dn11ZJuqXFfAGqs0vfsE9z95ARj+yVNyLujmS2StKjC7QCokao/oHN3Tx3g4u5tktqk5j4QBhjpKh16O2BmEyUpuzxYu5YA1EOlYV8n6a7s+l2S1tamHQD1Ung8u5mtkXSdpPGSDkhaJumPkl6UNEXSXkm3u/upH+IN9li8jK+D1Jhxd3d3ct2i493vvPPOZH3NmjXJekrRHOmbN29O1i+77LJkPfW7XfTYd9xxR7K+Y8eOirddb3nHsxe+Z3f3vDPx/7iqjgA0FF+XBYIg7EAQhB0IgrADQRB2IAgOcR3h+vr6kvWiobcFCxYk61u3bk3WU0N/CxcuTK47ZcqUZL1Iavjr8OHDyXWPHj2arI8ePTpZr+ehv5Vizw4EQdiBIAg7EARhB4Ig7EAQhB0IgrADQTBl8wj3+uuvJ+sXXXRRsr579+5kfcyYMcn65Zdfnlsrmuq66HTNe/fuTdaff/753NratelTMHR0dCTrRdNwl4kpm4HgCDsQBGEHgiDsQBCEHQiCsANBEHYgCMbZR7ii466vuOKKZP3BBx9M1m+5JT3NX+r3a/v27cl177333mR9586dyXrRNNwjFePsQHCEHQiCsANBEHYgCMIOBEHYgSAIOxBEmPPGt7S0JOvNfHxyNYr+XZMnT07WU9NBD8UHH3yQW5s/f35y3SNHjlS1bXxX4Z7dzJ4zs4Nmtm3AsuVm1mFmm7OfefVtE0C1hvIy/jeSbhxk+a/c/arsJ306FAClKwy7u2+Q9EUDegFQR9V8QPeAmW3JXuaPy7uTmS0ys3Yza69iWwCqVGnYV0m6RNJVkjol/TLvju7e5u6z3X12hdsCUAMVhd3dD7j7CXfvk/SMpDm1bQtArVUUdjObOODmfEnb8u4LoDkUjrOb2RpJ10kab2b7JC2TdJ2ZXSXJJe2RdH8de6yJkTqOXmTixInJ+n333ZesX3rppcn6/v37k/Vly5bl1hhHb6zCsLt76yCLn61DLwDqiK/LAkEQdiAIwg4EQdiBIAg7EESYQ1xHsosvvji3tmXLluS6Y8eOTdaLhtZeffXVZP3dd99N1qthNugZk4dU7+vrq3U7TY89OxAEYQeCIOxAEIQdCIKwA0EQdiAIwg4EwTj7MFA0Fr5hw4aK1y069PeNN95I1h9++OFkvaenJ1lPKRpHX7x4cbI+a9as3Nrdd9+dXHckHhLNnh0IgrADQRB2IAjCDgRB2IEgCDsQBGEHgmCcfRiYMGFCst7R0ZFbKzqV9Ntvv52sr1q1Klk/duxYsp5SNI5+9dVXJ+uPP/54sp6abvr++9NnPz969GiyPhyxZweCIOxAEIQdCIKwA0EQdiAIwg4EQdiBIBhnHwY++eSTZH3u3Lm5taeffjq57saNG5P1ommVW1pakvXUWPqoUel9zYIFC5L1MWPGJOvunlubMmVKct2PP/44WR+OCvfsZjbZzP5sZtvN7CMz+3m2/HwzW29mO7PLcfVvF0ClhvIyvlfSL9x9pqR/lPQzM5spaYmkt9x9uqS3stsAmlRh2N290903Zde7JO2QNEnSzZJWZ3dbLemWejUJoHqn9Z7dzC6W9CNJf5U0wd07s9J+SYN+gdvMFklaVHmLAGphyJ/Gm9lYSX+QtNjd/z6w5v2fhAz6aYi7t7n7bHefXVWnAKoypLCb2ZnqD/rv3P3lbPEBM5uY1SdKOlifFgHUQuHLeOsfO3lW0g53XzGgtE7SXZKeyC7X1qVDFOrq6sqtFU1NvHTp0mT9+PHjyXrq8FpJevPNN3NrqammJWnGjBnJempoTZK+/PLL3FpnZ2dubaQaynv2f5L0L5K2mtnmbNkj6g/5i2a2UNJeSbfXp0UAtVAYdnd/W1LeNyN+XNt2ANQLX5cFgiDsQBCEHQiCsANBEHYgCA5xHQGmTp2aW2ttba3qsVeuXJmsF51K+sknn8ytvfPOO8l1u7u7k/XUOLokPfrooxWvOxKxZweCIOxAEIQdCIKwA0EQdiAIwg4EQdiBIBhnHwE+/PDD0radmhZZSp9qur29Pblu0bTJqXF0qfgU3NGwZweCIOxAEIQdCIKwA0EQdiAIwg4EQdiBIKzo3Ns13ZhZ4zY2gpx99tnJetEx5dUoOu/8Qw89lKw/9dRTtWznO4qmfC7qfaRy90HPBs2eHQiCsANBEHYgCMIOBEHYgSAIOxAEYQeCGMr87JMl/VbSBEkuqc3df21myyXdJ+nz7K6PuPvr9Wo0sqJ5zFNzqBcdb37o0KFkfdKkScl6T09Psl5PUcfRKzWUk1f0SvqFu28ys3MlvW9m67Par9z9P+rXHoBaGcr87J2SOrPrXWa2Q1L6zz2ApnNa79nN7GJJP5L012zRA2a2xcyeM7NxOessMrN2M0ufgwhAXQ057GY2VtIfJC12979LWiXpEklXqX/P/8vB1nP3Nnef7e6za9AvgAoNKexmdqb6g/47d39Zktz9gLufcPc+Sc9ImlO/NgFUqzDsZmaSnpW0w91XDFg+ccDd5kvaVvv2ANRK4SGuZnaNpL9I2irp5FjHI5Ja1f8S3iXtkXR/9mFe6rE4xLUC/X9v86VO19zb21vrdtDk8g5x5Xj2YYCw43RwPDsQHGEHgiDsQBCEHQiCsANBEHYgCIbegGEmNRTr7gy9AdERdiAIwg4EQdiBIAg7EARhB4Ig7EAQQzm7bC0dkrR3wO3x2bJm1Ky9NWtfEr1V6rR6K/huzEV5hYZ+qeZ7Gzdrb9Zz0zVrb83al0RvlWpUb7yMB4Ig7EAQZYe9reTtpzRrb83al0RvlWpIb6W+ZwfQOGXv2QE0CGEHgigl7GZ2o5n9r5ntMrMlZfSQx8z2mNlWM9tc9vx02Rx6B81s24Bl55vZejPbmV0OOsdeSb0tN7OO7LnbbGbzSuptspn92cy2m9lHZvbzbHmpz12ir4Y8bw1/z25mLZL+T9I/S9on6T1Jre6+vaGN5DCzPZJmu3vpX8Aws2sldUv6rbv/Q7bs3yV94e5PZH8ox7n7vzZJb8sldZc9jXc2W9HEgdOMS7pF0t0q8blL9HW7GvC8lbFnnyNpl7vvdvceSb+XdHMJfTQ9d98g6YtTFt8saXV2fbX6f1kaLqe3puDune6+KbveJenkNOOlPneJvhqijLBPkvS3Abf3qbnme3dJfzKz981sUdnNDGLCgGm29kuaUGYzgyicxruRTplmvGmeu0qmP68WH9B93zXuPkvSXEk/y16uNiXvfw/WTGOnQ5rGu1EGmWb8W2U+d5VOf16tMsLeIWnygNs/zJY1BXfvyC4PSnpFzTcV9YGTM+hmlwdL7udbzTSN92DTjKsJnrsypz8vI+zvSZpuZlPNbLSkn0paV0If32NmY7IPTmRmYyT9RM03FfU6SXdl1++StLbEXr6jWabxzptmXCU/d6VPf56derahP5Lmqf8T+U8kPVpGDzl9TZP0YfbzUdm9SVqj/pd1x9X/2cZCST+Q9JaknZLelHR+E/X2X+qf2nuL+oM1saTerlH/S/QtkjZnP/PKfu4SfTXkeePrskAQfEAHBEHYgSAIOxAEYQeCIOxAEIQdCIKwA0H8P48vRVbufjR4AAAAAElFTkSuQmCC\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":["disc = checkpoint.discriminator\n","disc.summary()"],"metadata":{"id":"81pDU2CZl5TE","executionInfo":{"status":"ok","timestamp":1669828219986,"user_tz":-330,"elapsed":42,"user":{"displayName":"ROHAN DUBEY","userId":"11200230061670192260"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"832f2856-ed31-4a07-8f2b-14f495b6bf22"},"execution_count":9,"outputs":[{"output_type":"stream","name":"stdout","text":["Model: \"sequential_1\"\n","_________________________________________________________________\n"," Layer (type)                Output Shape              Param #   \n","=================================================================\n"," conv2d (Conv2D)             (None, 14, 14, 64)        1664      \n","                                                                 \n"," leaky_re_lu_3 (LeakyReLU)   (None, 14, 14, 64)        0         \n","                                                                 \n"," dropout (Dropout)           (None, 14, 14, 64)        0         \n","                                                                 \n"," conv2d_1 (Conv2D)           (None, 7, 7, 128)         204928    \n","                                                                 \n"," leaky_re_lu_4 (LeakyReLU)   (None, 7, 7, 128)         0         \n","                                                                 \n"," dropout_1 (Dropout)         (None, 7, 7, 128)         0         \n","                                                                 \n"," flatten (Flatten)           (None, 6272)              0         \n","                                                                 \n"," dense_1 (Dense)             (None, 1)                 6273      \n","                                                                 \n","=================================================================\n","Total params: 212,865\n","Trainable params: 212,865\n","Non-trainable params: 0\n","_________________________________________________________________\n"]}]},{"cell_type":"code","source":["val = disc(sample)\n","print (val)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"41f9NtMvEEv_","executionInfo":{"status":"ok","timestamp":1669828219987,"user_tz":-330,"elapsed":30,"user":{"displayName":"ROHAN DUBEY","userId":"11200230061670192260"}},"outputId":"3bfae64c-216d-4bfd-84ec-72fb36930cbb"},"execution_count":10,"outputs":[{"output_type":"stream","name":"stdout","text":["tf.Tensor([[-0.37809232]], shape=(1, 1), dtype=float32)\n"]}]},{"cell_type":"markdown","source":["Now, we transfer the weights of discriminator to a new model, with the only difference being that the new model's last dense layer has 10 parameters instead of 1."],"metadata":{"id":"nc63XVrwEIfG"}},{"cell_type":"code","source":["new_model = Sequential()\n","\n","for i in range(len(disc.layers) - 1):\n","    new_model.add(disc.layers[i])\n","\n","# Freeze the layers \n","for layer in new_model.layers:\n","    layer.trainable = True  #Unfreezed as True\n","# new_model.add(Dense(128))\n","# new_model.add(Dense(10, activation='softmax'))\n","new_model.summary()"],"metadata":{"id":"zd_ernVsl5P0","executionInfo":{"status":"ok","timestamp":1669828219987,"user_tz":-330,"elapsed":26,"user":{"displayName":"ROHAN DUBEY","userId":"11200230061670192260"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"6e6506ee-28db-47b1-bdfe-f1cb5b062754"},"execution_count":11,"outputs":[{"output_type":"stream","name":"stdout","text":["Model: \"sequential_2\"\n","_________________________________________________________________\n"," Layer (type)                Output Shape              Param #   \n","=================================================================\n"," conv2d (Conv2D)             (None, 14, 14, 64)        1664      \n","                                                                 \n"," leaky_re_lu_3 (LeakyReLU)   (None, 14, 14, 64)        0         \n","                                                                 \n"," dropout (Dropout)           (None, 14, 14, 64)        0         \n","                                                                 \n"," conv2d_1 (Conv2D)           (None, 7, 7, 128)         204928    \n","                                                                 \n"," leaky_re_lu_4 (LeakyReLU)   (None, 7, 7, 128)         0         \n","                                                                 \n"," dropout_1 (Dropout)         (None, 7, 7, 128)         0         \n","                                                                 \n"," flatten (Flatten)           (None, 6272)              0         \n","                                                                 \n","=================================================================\n","Total params: 206,592\n","Trainable params: 206,592\n","Non-trainable params: 0\n","_________________________________________________________________\n"]}]},{"cell_type":"code","source":["# view the initialized weights and bias of the last dense layer; weights are uniformly randomly generated \n","# and biases are all zeroes by default\n","new_model.layers[-1].weights"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"TNMbK2kvl5NL","outputId":"65afa115-9bfe-4691-9c0b-25ebd510bf5f","executionInfo":{"status":"ok","timestamp":1669828219988,"user_tz":-330,"elapsed":20,"user":{"displayName":"ROHAN DUBEY","userId":"11200230061670192260"}}},"execution_count":12,"outputs":[{"output_type":"execute_result","data":{"text/plain":["[]"]},"metadata":{},"execution_count":12}]},{"cell_type":"code","source":["(train_img, train_lb), (test_img, test_lb) = tf.keras.datasets.mnist.load_data()\n","\n","print(train_img.shape, train_lb.shape, test_img.shape, test_lb.shape, '\\n')\n","\n","# set random seed\n","np.random.seed(42)\n","trainsize = 10000  # number of training images we use, selected randomly\n","randtrain = np.random.choice(train_img.shape[0], trainsize, replace=False)\n","X_train, y_train = train_img[randtrain], train_lb[randtrain]\n","\n","np.random.seed(10)\n","testsize = 10000  # number of testing images we use, selected randomly\n","randtest = np.random.choice(test_img.shape[0], testsize, replace=False)\n","X_test, y_test = test_img[randtest], test_lb[randtest]\n","\n","print(X_train.shape, y_train.shape, X_test.shape, y_test.shape)"],"metadata":{"id":"0AQXyczyl6Ah","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1669828221939,"user_tz":-330,"elapsed":1969,"user":{"displayName":"ROHAN DUBEY","userId":"11200230061670192260"}},"outputId":"db96e580-d86f-4752-92c0-1f51f67f3b85"},"execution_count":13,"outputs":[{"output_type":"stream","name":"stdout","text":["Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n","11490434/11490434 [==============================] - 1s 0us/step\n","(60000, 28, 28) (60000,) (10000, 28, 28) (10000,) \n","\n","(10000, 28, 28) (10000,) (10000, 28, 28) (10000,)\n"]}]},{"cell_type":"code","source":["train_images = X_train.reshape((trainsize, 28, 28, 1))\n","train_images = (train_images.astype('float32') - 127.5) / 127.5\n","\n","test_images = X_test.reshape((testsize, 28, 28, 1))\n","test_images = (test_images.astype('float32') - 127.5) / 127.5\n","\n","train_labels = to_categorical(y_train)\n","test_labels = to_categorical(y_test)"],"metadata":{"id":"LvKJSkUpHrFk","executionInfo":{"status":"ok","timestamp":1669828221939,"user_tz":-330,"elapsed":6,"user":{"displayName":"ROHAN DUBEY","userId":"11200230061670192260"}}},"execution_count":14,"outputs":[]},{"cell_type":"code","source":["import skimage.data as skid\n","import cv2\n","import pylab as plt\n","import numpy as np\n","import cv2\n","from sklearn.cluster import MiniBatchKMeans\n","import pickle\n","import tqdm.notebook as tqdm\n","\n","def generate_vector(gray):\n","  Detector = cv2.xfeatures2d.SIFT_create() ## cv2.FastFeatureDetector.create()\n","  Descriptor = cv2.xfeatures2d.SIFT_create()\n","  vector_64 = np.zeros(64,dtype='uint8')\n","  step_size = 5\n","  kp = [cv2.KeyPoint(x, y, step_size) for y in range(0, gray.shape[0], step_size) \n","                                      for x in range(0, gray.shape[1], step_size)]\n","  kp, desc = Descriptor.compute(gray, kp)\n","  kmeans = MiniBatchKMeans(n_clusters=8, n_init=10)\n","  kmeans.fit(desc)\n","  predictions = kmeans.predict(X=desc,  sample_weight=None)\n","  for pre in predictions:\n","    vector_64[pre] +=1\n","  return vector_64\n","\n","# multiple images train\n","stack_tr = generate_vector(X_train[0])\n","count = 0\n","for img in tqdm.tqdm(X_train[1:]):\n","    vector = generate_vector(img)\n","    stack_tr = np.vstack((stack_tr,vector))\n","    count +=1\n","print(\"Histogram Generated for training set! \")\n","# multiple images train\n","stack_ts = generate_vector(X_test[0])\n","count = 0\n","for img in tqdm.tqdm(X_test[1:]):\n","    vector = generate_vector(img)\n","    stack_ts = np.vstack((stack_ts,vector))\n","    count +=1\n","print(\"Histogram Generated for testing set! \")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":117,"referenced_widgets":["4d32160479a64a29985b9e71a6321bd0","d576783bdda34dd3a0856af2403b4a03","de164fb48a9941e290e78d3b36c4fa3b","7a4e9ccf431f45248fac32ad01a603e2","98eab2b1950f4516860d1128b8ae601f","006f04a08481462aa60cd498dfc4bfb4","c8dced23a9ab45c78352ed62703f57f8","a46b5e69220141fd9ba612057206c765","f754f23bd2d44b3abb24bce5868c4918","afddb489a6b24e9e86b1a9fb9e3e638a","9e1a19b0837a4157b2ffcf407ee3f2fb","39f91c47cd904444a2f778f00f0a7b64","cc8e5be2c07a4e08a2fbf8d5bde7ab8e","51c4b1154d1b41f6b9752f1c46fb4bc2","63794ea10ed845bab494933485e7be83","3b6e99dedb324bf49022439313caf836","0fe85b59f91f43f2be9aadbc7501f89d","fe7ebb29763b488d946cdc1b81285e94","5a1d06b23359448fb29852ff8225b664","c8c55f0ad3584c2d9cc5f1da3fb85952","bf3ace9986ff478db7c90d1eb0b375c8","71d0e171ecbd4f68ae62000353b24b02"]},"id":"Lfx_sT2pF9DJ","executionInfo":{"status":"ok","timestamp":1669828979898,"user_tz":-330,"elapsed":757964,"user":{"displayName":"ROHAN DUBEY","userId":"11200230061670192260"}},"outputId":"7cfd3c0f-40e8-4c9d-e7f5-a5b8a3f32c2b"},"execution_count":15,"outputs":[{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/9999 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"4d32160479a64a29985b9e71a6321bd0"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Histogram Generated for training set! \n"]},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/9999 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"39f91c47cd904444a2f778f00f0a7b64"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Histogram Generated for testing set! \n"]}]},{"cell_type":"code","source":["# np.save(\"/content/drive/MyDrive/Colab Notebooks/SIFT+GAN handwriting/MNIST_SIFT_histogram_train.npy\",stack_tr)\n","# np.save(\"/content/drive/MyDrive/Colab Notebooks/SIFT+GAN handwriting/MNIST_SIFT_histogram_test.npy\",stack_ts)\n","#\n","stack_tr = np.load(\"/content/drive/MyDrive/Colab Notebooks/SIFT+GAN handwriting/MNIST_SIFT_histogram_train.npy\")\n","stack_ts = np.load(\"/content/drive/MyDrive/Colab Notebooks/SIFT+GAN handwriting/MNIST_SIFT_histogram_test.npy\")"],"metadata":{"id":"tm5HsaXY-XWp","executionInfo":{"status":"ok","timestamp":1669828981187,"user_tz":-330,"elapsed":1295,"user":{"displayName":"ROHAN DUBEY","userId":"11200230061670192260"}}},"execution_count":16,"outputs":[]},{"cell_type":"code","source":["def ExtractFeatures_Layer(dim):\n","    model = Sequential()\n","    model.add(Dense(64,input_dim=dim,kernel_regularizer=l2(0.01)\n","                    ))\n","    model.add(Dropout(0.5))\n","    return model\n","DSIFT = ExtractFeatures_Layer(stack_tr.shape[1])\n","MergeModel = concatenate([new_model.output, DSIFT.output])"],"metadata":{"id":"0meqFPqkF9A8"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# new_model.add(Dense(10, activation='softmax'))\n","m = Dense(32, activation='relu')(MergeModel)\n","m = Dropout(0.5)(m)\n","m = Dense(10, activation='softmax')(m)\n","\n","fin_model = Model(inputs=[new_model.input, DSIFT.input], outputs=m)\n","fin_model.summary()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Z2WWe08CGbGI","executionInfo":{"status":"ok","timestamp":1669789215509,"user_tz":-330,"elapsed":676,"user":{"displayName":"ROHAN DUBEY","userId":"11200230061670192260"}},"outputId":"9464a6a1-87a1-4b87-9685-f9a2b824e8a2"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Model: \"model\"\n","__________________________________________________________________________________________________\n"," Layer (type)                   Output Shape         Param #     Connected to                     \n","==================================================================================================\n"," conv2d_input (InputLayer)      [(None, 28, 28, 1)]  0           []                               \n","                                                                                                  \n"," conv2d (Conv2D)                (None, 14, 14, 64)   1664        ['conv2d_input[0][0]']           \n","                                                                                                  \n"," leaky_re_lu_3 (LeakyReLU)      (None, 14, 14, 64)   0           ['conv2d[1][0]']                 \n","                                                                                                  \n"," dropout (Dropout)              (None, 14, 14, 64)   0           ['leaky_re_lu_3[1][0]']          \n","                                                                                                  \n"," conv2d_1 (Conv2D)              (None, 7, 7, 128)    204928      ['dropout[1][0]']                \n","                                                                                                  \n"," leaky_re_lu_4 (LeakyReLU)      (None, 7, 7, 128)    0           ['conv2d_1[1][0]']               \n","                                                                                                  \n"," dense_2_input (InputLayer)     [(None, 64)]         0           []                               \n","                                                                                                  \n"," dropout_1 (Dropout)            (None, 7, 7, 128)    0           ['leaky_re_lu_4[1][0]']          \n","                                                                                                  \n"," dense_2 (Dense)                (None, 64)           4160        ['dense_2_input[0][0]']          \n","                                                                                                  \n"," flatten (Flatten)              (None, 6272)         0           ['dropout_1[1][0]']              \n","                                                                                                  \n"," dropout_2 (Dropout)            (None, 64)           0           ['dense_2[0][0]']                \n","                                                                                                  \n"," concatenate (Concatenate)      (None, 6336)         0           ['flatten[1][0]',                \n","                                                                  'dropout_2[0][0]']              \n","                                                                                                  \n"," dense_3 (Dense)                (None, 32)           202784      ['concatenate[0][0]']            \n","                                                                                                  \n"," dropout_3 (Dropout)            (None, 32)           0           ['dense_3[0][0]']                \n","                                                                                                  \n"," dense_4 (Dense)                (None, 10)           330         ['dropout_3[0][0]']              \n","                                                                                                  \n","==================================================================================================\n","Total params: 413,866\n","Trainable params: 413,866\n","Non-trainable params: 0\n","__________________________________________________________________________________________________\n"]}]},{"cell_type":"code","source":["fin_model.compile(Adam(learning_rate=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-7),\n","                  loss='categorical_crossentropy',\n","                  metrics=['accuracy',tf.keras.metrics.CategoricalCrossentropy()])"],"metadata":{"id":"PF3zGWrAIPc_"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["train_images.shape, stack_tr.shape, train_labels.shape, stack_ts.shape"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"fgG29TvLHWD8","executionInfo":{"status":"ok","timestamp":1669789215510,"user_tz":-330,"elapsed":9,"user":{"displayName":"ROHAN DUBEY","userId":"11200230061670192260"}},"outputId":"4438624f-ea6e-47ba-e83c-c851eef92438"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["((10000, 28, 28, 1), (10000, 64), (10000, 10), (10000, 64))"]},"metadata":{},"execution_count":19}]},{"cell_type":"code","source":["filepath = \"/content/drive/MyDrive/Colab Notebooks/SIFT+GAN handwriting/GAN_SIFTNET_\"+\"MNIST\"+\"_best_weights.hdf5\"\n","early_stop = EarlyStopping(monitor='val_accuracy', patience=50,mode='max')\n","checkpoint = ModelCheckpoint(filepath, monitor='val_accuracy', verbose=1, save_best_only=True, mode='max')\n","callbacks_list = [checkpoint,early_stop]\n","\n","history2 = fin_model.fit([train_images,stack_tr], train_labels, batch_size=100, epochs=20\n","                        ,validation_data=([test_images,stack_ts], test_labels),\n","                         callbacks = callbacks_list,\n","                        ) "],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"WkwbM5t2F8-R","executionInfo":{"status":"ok","timestamp":1669789795331,"user_tz":-330,"elapsed":578596,"user":{"displayName":"ROHAN DUBEY","userId":"11200230061670192260"}},"outputId":"20a6c2c8-9d9a-4f8a-f9ed-e65bc9e55306"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/20\n","100/100 [==============================] - ETA: 0s - loss: 1.3737 - accuracy: 0.6604 - categorical_crossentropy: 1.0190\n","Epoch 1: val_accuracy improved from -inf to 0.91320, saving model to /content/drive/MyDrive/Colab Notebooks/SIFT+GAN handwriting/GAN_SIFTNET_MNIST_best_weights.hdf5\n","100/100 [==============================] - 68s 665ms/step - loss: 1.3737 - accuracy: 0.6604 - categorical_crossentropy: 1.0190 - val_loss: 0.4878 - val_accuracy: 0.9132 - val_categorical_crossentropy: 0.3178\n","Epoch 2/20\n","100/100 [==============================] - ETA: 0s - loss: 0.6597 - accuracy: 0.8227 - categorical_crossentropy: 0.5617\n","Epoch 2: val_accuracy improved from 0.91320 to 0.94000, saving model to /content/drive/MyDrive/Colab Notebooks/SIFT+GAN handwriting/GAN_SIFTNET_MNIST_best_weights.hdf5\n","100/100 [==============================] - 27s 272ms/step - loss: 0.6597 - accuracy: 0.8227 - categorical_crossentropy: 0.5617 - val_loss: 0.2636 - val_accuracy: 0.9400 - val_categorical_crossentropy: 0.2119\n","Epoch 3/20\n","100/100 [==============================] - ETA: 0s - loss: 0.4818 - accuracy: 0.8539 - categorical_crossentropy: 0.4483\n","Epoch 3: val_accuracy improved from 0.94000 to 0.95310, saving model to /content/drive/MyDrive/Colab Notebooks/SIFT+GAN handwriting/GAN_SIFTNET_MNIST_best_weights.hdf5\n","100/100 [==============================] - 26s 264ms/step - loss: 0.4818 - accuracy: 0.8539 - categorical_crossentropy: 0.4483 - val_loss: 0.1789 - val_accuracy: 0.9531 - val_categorical_crossentropy: 0.1578\n","Epoch 4/20\n","100/100 [==============================] - ETA: 0s - loss: 0.4046 - accuracy: 0.8723 - categorical_crossentropy: 0.3894\n","Epoch 4: val_accuracy improved from 0.95310 to 0.95970, saving model to /content/drive/MyDrive/Colab Notebooks/SIFT+GAN handwriting/GAN_SIFTNET_MNIST_best_weights.hdf5\n","100/100 [==============================] - 28s 276ms/step - loss: 0.4046 - accuracy: 0.8723 - categorical_crossentropy: 0.3894 - val_loss: 0.1452 - val_accuracy: 0.9597 - val_categorical_crossentropy: 0.1345\n","Epoch 5/20\n","100/100 [==============================] - ETA: 0s - loss: 0.3575 - accuracy: 0.8837 - categorical_crossentropy: 0.3496\n","Epoch 5: val_accuracy improved from 0.95970 to 0.96100, saving model to /content/drive/MyDrive/Colab Notebooks/SIFT+GAN handwriting/GAN_SIFTNET_MNIST_best_weights.hdf5\n","100/100 [==============================] - 26s 263ms/step - loss: 0.3575 - accuracy: 0.8837 - categorical_crossentropy: 0.3496 - val_loss: 0.1417 - val_accuracy: 0.9610 - val_categorical_crossentropy: 0.1362\n","Epoch 6/20\n","100/100 [==============================] - ETA: 0s - loss: 0.3262 - accuracy: 0.8957 - categorical_crossentropy: 0.3221\n","Epoch 6: val_accuracy improved from 0.96100 to 0.96430, saving model to /content/drive/MyDrive/Colab Notebooks/SIFT+GAN handwriting/GAN_SIFTNET_MNIST_best_weights.hdf5\n","100/100 [==============================] - 28s 278ms/step - loss: 0.3262 - accuracy: 0.8957 - categorical_crossentropy: 0.3221 - val_loss: 0.1204 - val_accuracy: 0.9643 - val_categorical_crossentropy: 0.1175\n","Epoch 7/20\n","100/100 [==============================] - ETA: 0s - loss: 0.3017 - accuracy: 0.8993 - categorical_crossentropy: 0.2995\n","Epoch 7: val_accuracy improved from 0.96430 to 0.96450, saving model to /content/drive/MyDrive/Colab Notebooks/SIFT+GAN handwriting/GAN_SIFTNET_MNIST_best_weights.hdf5\n","100/100 [==============================] - 27s 265ms/step - loss: 0.3017 - accuracy: 0.8993 - categorical_crossentropy: 0.2995 - val_loss: 0.1165 - val_accuracy: 0.9645 - val_categorical_crossentropy: 0.1150\n","Epoch 8/20\n","100/100 [==============================] - ETA: 0s - loss: 0.2838 - accuracy: 0.9058 - categorical_crossentropy: 0.2826\n","Epoch 8: val_accuracy improved from 0.96450 to 0.96930, saving model to /content/drive/MyDrive/Colab Notebooks/SIFT+GAN handwriting/GAN_SIFTNET_MNIST_best_weights.hdf5\n","100/100 [==============================] - 29s 294ms/step - loss: 0.2838 - accuracy: 0.9058 - categorical_crossentropy: 0.2826 - val_loss: 0.1060 - val_accuracy: 0.9693 - val_categorical_crossentropy: 0.1052\n","Epoch 9/20\n","100/100 [==============================] - ETA: 0s - loss: 0.2825 - accuracy: 0.9048 - categorical_crossentropy: 0.2818\n","Epoch 9: val_accuracy did not improve from 0.96930\n","100/100 [==============================] - 28s 280ms/step - loss: 0.2825 - accuracy: 0.9048 - categorical_crossentropy: 0.2818 - val_loss: 0.1005 - val_accuracy: 0.9692 - val_categorical_crossentropy: 0.1001\n","Epoch 10/20\n","100/100 [==============================] - ETA: 0s - loss: 0.2530 - accuracy: 0.9137 - categorical_crossentropy: 0.2527\n","Epoch 10: val_accuracy did not improve from 0.96930\n","100/100 [==============================] - 26s 260ms/step - loss: 0.2530 - accuracy: 0.9137 - categorical_crossentropy: 0.2527 - val_loss: 0.0994 - val_accuracy: 0.9693 - val_categorical_crossentropy: 0.0992\n","Epoch 11/20\n","100/100 [==============================] - ETA: 0s - loss: 0.2416 - accuracy: 0.9152 - categorical_crossentropy: 0.2414\n","Epoch 11: val_accuracy improved from 0.96930 to 0.97110, saving model to /content/drive/MyDrive/Colab Notebooks/SIFT+GAN handwriting/GAN_SIFTNET_MNIST_best_weights.hdf5\n","100/100 [==============================] - 26s 263ms/step - loss: 0.2416 - accuracy: 0.9152 - categorical_crossentropy: 0.2414 - val_loss: 0.0961 - val_accuracy: 0.9711 - val_categorical_crossentropy: 0.0959\n","Epoch 12/20\n","100/100 [==============================] - ETA: 0s - loss: 0.2430 - accuracy: 0.9159 - categorical_crossentropy: 0.2428\n","Epoch 12: val_accuracy improved from 0.97110 to 0.97310, saving model to /content/drive/MyDrive/Colab Notebooks/SIFT+GAN handwriting/GAN_SIFTNET_MNIST_best_weights.hdf5\n","100/100 [==============================] - 26s 262ms/step - loss: 0.2430 - accuracy: 0.9159 - categorical_crossentropy: 0.2428 - val_loss: 0.0945 - val_accuracy: 0.9731 - val_categorical_crossentropy: 0.0943\n","Epoch 13/20\n","100/100 [==============================] - ETA: 0s - loss: 0.2268 - accuracy: 0.9214 - categorical_crossentropy: 0.2267\n","Epoch 13: val_accuracy improved from 0.97310 to 0.97440, saving model to /content/drive/MyDrive/Colab Notebooks/SIFT+GAN handwriting/GAN_SIFTNET_MNIST_best_weights.hdf5\n","100/100 [==============================] - 26s 263ms/step - loss: 0.2268 - accuracy: 0.9214 - categorical_crossentropy: 0.2267 - val_loss: 0.0866 - val_accuracy: 0.9744 - val_categorical_crossentropy: 0.0865\n","Epoch 14/20\n","100/100 [==============================] - ETA: 0s - loss: 0.2193 - accuracy: 0.9240 - categorical_crossentropy: 0.2192\n","Epoch 14: val_accuracy did not improve from 0.97440\n","100/100 [==============================] - 26s 261ms/step - loss: 0.2193 - accuracy: 0.9240 - categorical_crossentropy: 0.2192 - val_loss: 0.0852 - val_accuracy: 0.9734 - val_categorical_crossentropy: 0.0851\n","Epoch 15/20\n","100/100 [==============================] - ETA: 0s - loss: 0.2136 - accuracy: 0.9299 - categorical_crossentropy: 0.2135\n","Epoch 15: val_accuracy did not improve from 0.97440\n","100/100 [==============================] - 26s 261ms/step - loss: 0.2136 - accuracy: 0.9299 - categorical_crossentropy: 0.2135 - val_loss: 0.0928 - val_accuracy: 0.9714 - val_categorical_crossentropy: 0.0927\n","Epoch 16/20\n","100/100 [==============================] - ETA: 0s - loss: 0.2232 - accuracy: 0.9250 - categorical_crossentropy: 0.2231\n","Epoch 16: val_accuracy improved from 0.97440 to 0.97460, saving model to /content/drive/MyDrive/Colab Notebooks/SIFT+GAN handwriting/GAN_SIFTNET_MNIST_best_weights.hdf5\n","100/100 [==============================] - 28s 285ms/step - loss: 0.2232 - accuracy: 0.9250 - categorical_crossentropy: 0.2231 - val_loss: 0.0845 - val_accuracy: 0.9746 - val_categorical_crossentropy: 0.0844\n","Epoch 17/20\n","100/100 [==============================] - ETA: 0s - loss: 0.2198 - accuracy: 0.9267 - categorical_crossentropy: 0.2197\n","Epoch 17: val_accuracy did not improve from 0.97460\n","100/100 [==============================] - 26s 260ms/step - loss: 0.2198 - accuracy: 0.9267 - categorical_crossentropy: 0.2197 - val_loss: 0.0919 - val_accuracy: 0.9737 - val_categorical_crossentropy: 0.0918\n","Epoch 18/20\n","100/100 [==============================] - ETA: 0s - loss: 0.1987 - accuracy: 0.9324 - categorical_crossentropy: 0.1986\n","Epoch 18: val_accuracy improved from 0.97460 to 0.97500, saving model to /content/drive/MyDrive/Colab Notebooks/SIFT+GAN handwriting/GAN_SIFTNET_MNIST_best_weights.hdf5\n","100/100 [==============================] - 28s 283ms/step - loss: 0.1987 - accuracy: 0.9324 - categorical_crossentropy: 0.1986 - val_loss: 0.0937 - val_accuracy: 0.9750 - val_categorical_crossentropy: 0.0936\n","Epoch 19/20\n","100/100 [==============================] - ETA: 0s - loss: 0.2010 - accuracy: 0.9284 - categorical_crossentropy: 0.2009\n","Epoch 19: val_accuracy improved from 0.97500 to 0.97600, saving model to /content/drive/MyDrive/Colab Notebooks/SIFT+GAN handwriting/GAN_SIFTNET_MNIST_best_weights.hdf5\n","100/100 [==============================] - 26s 263ms/step - loss: 0.2010 - accuracy: 0.9284 - categorical_crossentropy: 0.2009 - val_loss: 0.0845 - val_accuracy: 0.9760 - val_categorical_crossentropy: 0.0844\n","Epoch 20/20\n","100/100 [==============================] - ETA: 0s - loss: 0.1892 - accuracy: 0.9333 - categorical_crossentropy: 0.1891\n","Epoch 20: val_accuracy did not improve from 0.97600\n","100/100 [==============================] - 26s 260ms/step - loss: 0.1892 - accuracy: 0.9333 - categorical_crossentropy: 0.1891 - val_loss: 0.0906 - val_accuracy: 0.9753 - val_categorical_crossentropy: 0.0905\n"]}]},{"cell_type":"code","source":["accuracy = history2.history['accuracy']\n","val_accuracy = history2.history['val_accuracy']\n","\n","plt.figure()\n","plt.plot(accuracy, label='accuracy')\n","plt.plot(val_accuracy, label = 'val_accuracy')\n","plt.xlabel('Epoch')\n","plt.ylabel('Accuracy')\n","plt.ylim([0.3, 1])\n","plt.legend(loc='lower right')\n","plt.show()"],"metadata":{"id":"InnQ47D8F82w","colab":{"base_uri":"https://localhost:8080/","height":283},"executionInfo":{"status":"ok","timestamp":1669790073628,"user_tz":-330,"elapsed":18,"user":{"displayName":"ROHAN DUBEY","userId":"11200230061670192260"}},"outputId":"80e0f95f-dadc-4978-9821-264d023a8b8a"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":["loss = history2.history['loss']\n","val_loss = history2.history['val_loss']\n","\n","plt.figure()\n","plt.plot(loss, label='loss')\n","plt.plot(val_loss, label = 'val_loss')\n","plt.xlabel('Epoch')\n","plt.ylabel('Loss')\n","plt.legend(loc='upper right')\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":279},"id":"33EHdh9URWcE","executionInfo":{"status":"ok","timestamp":1669792091214,"user_tz":-330,"elapsed":1101,"user":{"displayName":"ROHAN DUBEY","userId":"11200230061670192260"}},"outputId":"bd159a06-6179-4b71-bcb5-7836e54df855"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":["We can see that, using the tuned discriminator model, both training accuracy and testing are approximately 0.90 after 15 epochs."],"metadata":{"id":"kuIT4USFEZ7F"}},{"cell_type":"code","source":["stack_ts[i].shape, stack_ts[i].reshape(1,64).shape, test_images[i].shape, test_images[i].reshape(1,28,28,1).shape"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"_iIDnrQlQDGY","executionInfo":{"status":"ok","timestamp":1669790276042,"user_tz":-330,"elapsed":4,"user":{"displayName":"ROHAN DUBEY","userId":"11200230061670192260"}},"outputId":"df34b4ec-679e-41f6-b5de-0d06e716d476"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["((64,), (1, 64), (28, 28, 1), (1, 28, 28, 1))"]},"metadata":{},"execution_count":34}]},{"cell_type":"code","source":["# demonstration: predict the ith test digit\n","i = 15\n","prediction = fin_model.predict([test_images[i].reshape(1,28,28,1),stack_ts[i].reshape(1,64)])\n","\n","# get probability distribution and classification of the test digit\n","print(prediction)\n","print('prediction:', np.argmax(prediction))\n","\n","# draw the barplot\n","plt.figure()\n","plt.bar(np.arange(0,10).astype('str'), prediction[0,:])\n","plt.show()"],"metadata":{"id":"R1K4JKmEoVx7","colab":{"base_uri":"https://localhost:8080/","height":338},"executionInfo":{"status":"ok","timestamp":1669790289927,"user_tz":-330,"elapsed":28,"user":{"displayName":"ROHAN DUBEY","userId":"11200230061670192260"}},"outputId":"de2092a3-3077-40bd-9882-b86a20adfaeb"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 140ms/step\n","[[4.0532642e-12 6.1615590e-10 9.9997926e-01 1.8435341e-05 3.8530305e-15\n","  1.6731408e-14 3.2325441e-15 2.2798097e-06 1.2181883e-08 7.3216796e-14]]\n","prediction: 2\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":["# show the actual ith digit\n","print('actual label:', np.argmax(test_labels[i]))\n","plt.figure()\n","plt.imshow(test_images[i,:,:,0], cmap='gray')\n","plt.show()"],"metadata":{"id":"fhckSRTIoXEi","colab":{"base_uri":"https://localhost:8080/","height":283},"executionInfo":{"status":"ok","timestamp":1669790300654,"user_tz":-330,"elapsed":1053,"user":{"displayName":"ROHAN DUBEY","userId":"11200230061670192260"}},"outputId":"6020371f-b0be-40b1-c458-278c1a46a0d7"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["actual label: 2\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":["pred_labels = fin_model.predict([test_images,stack_ts]).argmax(axis=1)\n","true_labels = test_labels.argmax(axis=1)"],"metadata":{"id":"IG-VgKNqoYuj","colab":{"base_uri":"https://localhost:8080/"},"outputId":"bb2204a7-51f6-4311-92cd-6b6ac95c30f5","executionInfo":{"status":"ok","timestamp":1669790345744,"user_tz":-330,"elapsed":9294,"user":{"displayName":"ROHAN DUBEY","userId":"11200230061670192260"}}},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["313/313 [==============================] - 8s 27ms/step\n"]}]},{"cell_type":"code","source":["from sklearn.metrics import confusion_matrix\n","confusion_mat = confusion_matrix(true_labels, pred_labels)\n","confusion_mat"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"JZuxHfnFQxgA","executionInfo":{"status":"ok","timestamp":1669790508102,"user_tz":-330,"elapsed":10,"user":{"displayName":"ROHAN DUBEY","userId":"11200230061670192260"}},"outputId":"559dd9fd-5b17-4f32-a53b-a456111280a0"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[ 974,    0,    1,    0,    0,    0,    1,    1,    3,    0],\n","       [   0, 1127,    2,    2,    0,    0,    3,    1,    0,    0],\n","       [   6,    1, 1001,    2,    3,    0,    0,   10,    9,    0],\n","       [   0,    0,    8,  986,    0,    5,    0,    8,    2,    1],\n","       [   0,    2,    3,    0,  939,    0,    4,    1,    3,   30],\n","       [   2,    0,    0,    5,    0,  877,    4,    1,    2,    1],\n","       [  13,    3,    0,    0,    1,    3,  937,    0,    1,    0],\n","       [   2,    4,   12,    2,    0,    1,    0,  997,    2,    8],\n","       [   6,    0,    6,    3,    1,    1,    0,    3,  942,   12],\n","       [   2,    6,    0,    8,    2,    7,    1,    7,    3,  973]])"]},"metadata":{},"execution_count":49}]},{"cell_type":"code","source":["import pandas as pd\n","import seaborn as sns\n","\n","confusion_mat_norm = np.around(confusion_mat.astype('float') / np.atleast_2d(confusion_mat.sum(axis=1)).T, decimals=2)\n","\n","classes = np.arange(0,10).astype('str')\n","confusion_mat_df = pd.DataFrame(confusion_mat_norm,\n","                                index = classes, \n","                                columns = classes)\n","\n","figure = plt.figure()\n","sns.heatmap(confusion_mat_df, annot=True, cmap=plt.cm.Blues)\n","plt.tight_layout()\n","plt.ylabel('True label')\n","plt.xlabel('Predicted label')\n","plt.show()"],"metadata":{"id":"XKXDeR3nocJB","colab":{"base_uri":"https://localhost:8080/","height":311},"executionInfo":{"status":"ok","timestamp":1669790509388,"user_tz":-330,"elapsed":13,"user":{"displayName":"ROHAN DUBEY","userId":"11200230061670192260"}},"outputId":"7f9c389e-4dd3-414a-f9f3-75bc306a0996"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 2 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":[],"metadata":{"id":"5FqM32UOoucd"},"execution_count":null,"outputs":[]}],"metadata":{"colab":{"provenance":[],"authorship_tag":"ABX9TyMLYWMDekp0BLVydJS5h4uE"},"gpuClass":"standard","kernelspec":{"display_name":"Python 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