GAN EMNIST 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":1669826436830,"user_tz":-330,"elapsed":28469,"user":{"displayName":"ROHAN DUBEY","userId":"11200230061670192260"}},"outputId":"c0680e9c-ef8c-498e-f98d-4caa2b0a5125"},"execution_count":1,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}]},{"cell_type":"code","execution_count":2,"metadata":{"id":"icQNarJ2L3CI","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1669826448344,"user_tz":-330,"elapsed":8019,"user":{"displayName":"ROHAN DUBEY","userId":"11200230061670192260"}},"outputId":"6733e2a1-a3f4-4e57-e7a6-9ea8cc523516"},"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Collecting emnist\n","  Downloading emnist-0.0-py3-none-any.whl (7.3 kB)\n","Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from emnist) (4.64.1)\n","Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from emnist) (1.21.6)\n","Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from emnist) (2.23.0)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->emnist) (2022.9.24)\n","Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->emnist) (3.0.4)\n","Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->emnist) (2.10)\n","Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->emnist) (1.24.3)\n","Installing collected packages: emnist\n","Successfully installed emnist-0.0\n"]}],"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","!pip install emnist\n","\n","from IPython import display"]},{"cell_type":"code","execution_count":3,"metadata":{"id":"BOKI_3ROOytg","executionInfo":{"status":"ok","timestamp":1669826448344,"user_tz":-330,"elapsed":10,"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":1669826448345,"user_tz":-330,"elapsed":10,"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":1669826449038,"user_tz":-330,"elapsed":703,"user":{"displayName":"ROHAN DUBEY","userId":"11200230061670192260"}}},"execution_count":5,"outputs":[]},{"cell_type":"code","execution_count":6,"metadata":{"id":"dfVvU4PWMbPP","executionInfo":{"status":"ok","timestamp":1669826451321,"user_tz":-330,"elapsed":2,"user":{"displayName":"ROHAN DUBEY","userId":"11200230061670192260"}}},"outputs":[],"source":["checkpoint_dir = '/content/drive/MyDrive/Colab Notebooks/SIFT+GAN handwriting/training_checkpoints_EMNIST'\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":1669826459788,"user_tz":-330,"elapsed":7093,"user":{"displayName":"ROHAN DUBEY","userId":"11200230061670192260"}},"outputId":"446a2fa7-4b12-4de0-c6fa-38eb46573a0e"},"execution_count":7,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<tensorflow.python.training.tracking.util.CheckpointLoadStatus at 0x7fcf15b59d50>"]},"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":1669826463753,"user_tz":-330,"elapsed":13,"user":{"displayName":"ROHAN DUBEY","userId":"11200230061670192260"}},"outputId":"522780de-975a-49f6-da81-e9a4b95332a8"},"execution_count":8,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.image.AxesImage at 0x7fcf109a2350>"]},"metadata":{},"execution_count":8},{"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":["disc = checkpoint.discriminator\n","disc.summary()"],"metadata":{"id":"81pDU2CZl5TE","executionInfo":{"status":"ok","timestamp":1669826466572,"user_tz":-330,"elapsed":16,"user":{"displayName":"ROHAN DUBEY","userId":"11200230061670192260"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"5636f705-1e2a-4e8b-e864-7bf54fb21b8d"},"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":1669826470400,"user_tz":-330,"elapsed":1073,"user":{"displayName":"ROHAN DUBEY","userId":"11200230061670192260"}},"outputId":"a05c0b3b-ceff-48e0-fe8e-d0635ef24a74"},"execution_count":10,"outputs":[{"output_type":"stream","name":"stdout","text":["tf.Tensor([[-1.4654498]], 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","\n","new_model.summary()"],"metadata":{"id":"zd_ernVsl5P0","executionInfo":{"status":"ok","timestamp":1669826490742,"user_tz":-330,"elapsed":13,"user":{"displayName":"ROHAN DUBEY","userId":"11200230061670192260"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"b5aa69ce-848b-4d72-d437-3f4d1e068497"},"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":"b9a29717-2b28-41fd-b9f9-461f729a751d","executionInfo":{"status":"ok","timestamp":1669826491430,"user_tz":-330,"elapsed":8,"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":["from emnist import extract_training_samples, extract_test_samples\n","train_img, train_lb = extract_training_samples('letters')\n","test_img, test_lb = extract_test_samples('letters')\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 = 30000  # 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":1669826536842,"user_tz":-330,"elapsed":39854,"user":{"displayName":"ROHAN DUBEY","userId":"11200230061670192260"}},"outputId":"67dd2a3d-2db0-42f2-8359-7a9da4f54254"},"execution_count":13,"outputs":[{"output_type":"stream","name":"stderr","text":["Downloading emnist.zip: 536MB [00:36, 15.4MB/s]\n"]},{"output_type":"stream","name":"stdout","text":["(124800, 28, 28) (124800,) (20800, 28, 28) (20800,) \n","\n","(30000, 28, 28) (30000,) (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":1669826557236,"user_tz":-330,"elapsed":742,"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":["d5cd6efa5bb54d4aba25d262b9593995","6631bd7db96141b48225a072eeb9dd10","1ee64e3ef4054d62b03ab2c1723a59cd","646abdd6f7284bc78aa51c561008b6ca","57d3fd99827d4a7f956f9986120909ee","c5cd2ac8ead4430a894f856005e0372b","5184ecb81678444a9da2ef91896064b1","e5fb70b4519549de80cd1b9eb1990446","5a410611ca9547ed845dc039755c492c","8ecf70bce87b40a0ace8be17fabac669","72bfd75727a74025b3ca978924e91b60","1bd0903cec7b4fc0acbede8b8b9ff090","cad40234c50e4611a0387b9faa4bb5b7","392df437599b47a885c0e8409d88e825","f13b0d41078645aca6f4793cc47b32db","a3790cd13b3040b9ab778bbf2b952a1c","632cbc8545a84ba9a12fd9c4b35d334f","981be57cab754e1fb279bd1a7a164ede","305aabcaefde4df599617a71b26bfb4f","f1f0a28dac3844be8e7cdc094a7ceb7c","1794546b210444b79cc16b4549877997","148ea55010224408b13a2e3fc149260d"]},"id":"Lfx_sT2pF9DJ","executionInfo":{"status":"ok","timestamp":1669828060501,"user_tz":-330,"elapsed":1480880,"user":{"displayName":"ROHAN DUBEY","userId":"11200230061670192260"}},"outputId":"62ea5f34-d53a-4cd1-a820-a3d472694215"},"execution_count":15,"outputs":[{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/29999 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"d5cd6efa5bb54d4aba25d262b9593995"}},"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":"1bd0903cec7b4fc0acbede8b8b9ff090"}},"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/EMNIST_SIFT_histogram_train.npy\",stack_tr)\n","# np.save(\"/content/drive/MyDrive/Colab Notebooks/SIFT+GAN handwriting/EMNIST_SIFT_histogram_test.npy\",stack_ts)\n","#\n","stack_tr = np.load(\"/content/drive/MyDrive/Colab Notebooks/SIFT+GAN handwriting/EMNIST_SIFT_histogram_train.npy\")\n","stack_ts = np.load(\"/content/drive/MyDrive/Colab Notebooks/SIFT+GAN handwriting/EMNIST_SIFT_histogram_test.npy\")"],"metadata":{"id":"tm5HsaXY-XWp","executionInfo":{"status":"ok","timestamp":1669828085888,"user_tz":-330,"elapsed":4,"user":{"displayName":"ROHAN DUBEY","userId":"11200230061670192260"}}},"execution_count":17,"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","executionInfo":{"status":"ok","timestamp":1669828095241,"user_tz":-330,"elapsed":657,"user":{"displayName":"ROHAN DUBEY","userId":"11200230061670192260"}}},"execution_count":18,"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(27, 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":1669828095242,"user_tz":-330,"elapsed":42,"user":{"displayName":"ROHAN DUBEY","userId":"11200230061670192260"}},"outputId":"1676e2a8-e5e3-4353-9529-c918de1546b3"},"execution_count":19,"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, 27)           891         ['dropout_3[0][0]']              \n","                                                                                                  \n","==================================================================================================\n","Total params: 414,427\n","Trainable params: 414,427\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_","executionInfo":{"status":"ok","timestamp":1669828095968,"user_tz":-330,"elapsed":734,"user":{"displayName":"ROHAN DUBEY","userId":"11200230061670192260"}}},"execution_count":20,"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":1669828095969,"user_tz":-330,"elapsed":9,"user":{"displayName":"ROHAN DUBEY","userId":"11200230061670192260"}},"outputId":"a5b566ff-d12e-4e41-9808-ce08a576294b"},"execution_count":21,"outputs":[{"output_type":"execute_result","data":{"text/plain":["((30000, 28, 28, 1), (30000, 64), (30000, 27), (10000, 64))"]},"metadata":{},"execution_count":21}]},{"cell_type":"code","source":["filepath = \"/content/drive/MyDrive/Colab Notebooks/SIFT+GAN handwriting/GAN_SIFTNET_\"+\"EMNIST\"+\"_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":1669829516791,"user_tz":-330,"elapsed":1402492,"user":{"displayName":"ROHAN DUBEY","userId":"11200230061670192260"}},"outputId":"1498bb5c-b221-4269-f535-2ecd53b6166f"},"execution_count":24,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/20\n","300/300 [==============================] - ETA: 0s - loss: 2.0159 - accuracy: 0.4338 - categorical_crossentropy: 1.8951\n","Epoch 1: val_accuracy improved from -inf to 0.77920, saving model to /content/drive/MyDrive/Colab Notebooks/SIFT+GAN handwriting/GAN_SIFTNET_EMNIST_best_weights.hdf5\n","300/300 [==============================] - 69s 231ms/step - loss: 2.0159 - accuracy: 0.4338 - categorical_crossentropy: 1.8951 - val_loss: 0.8823 - val_accuracy: 0.7792 - val_categorical_crossentropy: 0.8626\n","Epoch 2/20\n","300/300 [==============================] - ETA: 0s - loss: 1.3192 - accuracy: 0.5860 - categorical_crossentropy: 1.3093\n","Epoch 2: val_accuracy improved from 0.77920 to 0.82060, saving model to /content/drive/MyDrive/Colab Notebooks/SIFT+GAN handwriting/GAN_SIFTNET_EMNIST_best_weights.hdf5\n","300/300 [==============================] - 69s 231ms/step - loss: 1.3192 - accuracy: 0.5860 - categorical_crossentropy: 1.3093 - val_loss: 0.6240 - val_accuracy: 0.8206 - val_categorical_crossentropy: 0.6196\n","Epoch 3/20\n","300/300 [==============================] - ETA: 0s - loss: 1.1597 - accuracy: 0.6270 - categorical_crossentropy: 1.1575\n","Epoch 3: val_accuracy improved from 0.82060 to 0.83360, saving model to /content/drive/MyDrive/Colab Notebooks/SIFT+GAN handwriting/GAN_SIFTNET_EMNIST_best_weights.hdf5\n","300/300 [==============================] - 70s 234ms/step - loss: 1.1597 - accuracy: 0.6270 - categorical_crossentropy: 1.1575 - val_loss: 0.5451 - val_accuracy: 0.8336 - val_categorical_crossentropy: 0.5441\n","Epoch 4/20\n","300/300 [==============================] - ETA: 0s - loss: 1.0527 - accuracy: 0.6587 - categorical_crossentropy: 1.0521\n","Epoch 4: val_accuracy improved from 0.83360 to 0.85330, saving model to /content/drive/MyDrive/Colab Notebooks/SIFT+GAN handwriting/GAN_SIFTNET_EMNIST_best_weights.hdf5\n","300/300 [==============================] - 71s 236ms/step - loss: 1.0527 - accuracy: 0.6587 - categorical_crossentropy: 1.0521 - val_loss: 0.4924 - val_accuracy: 0.8533 - val_categorical_crossentropy: 0.4921\n","Epoch 5/20\n","300/300 [==============================] - ETA: 0s - loss: 1.0015 - accuracy: 0.6741 - categorical_crossentropy: 1.0013\n","Epoch 5: val_accuracy improved from 0.85330 to 0.85680, saving model to /content/drive/MyDrive/Colab Notebooks/SIFT+GAN handwriting/GAN_SIFTNET_EMNIST_best_weights.hdf5\n","300/300 [==============================] - 69s 230ms/step - loss: 1.0015 - accuracy: 0.6741 - categorical_crossentropy: 1.0013 - val_loss: 0.4630 - val_accuracy: 0.8568 - val_categorical_crossentropy: 0.4628\n","Epoch 6/20\n","300/300 [==============================] - ETA: 0s - loss: 0.9598 - accuracy: 0.6826 - categorical_crossentropy: 0.9596\n","Epoch 6: val_accuracy improved from 0.85680 to 0.85730, saving model to /content/drive/MyDrive/Colab Notebooks/SIFT+GAN handwriting/GAN_SIFTNET_EMNIST_best_weights.hdf5\n","300/300 [==============================] - 69s 229ms/step - loss: 0.9598 - accuracy: 0.6826 - categorical_crossentropy: 0.9596 - val_loss: 0.4572 - val_accuracy: 0.8573 - val_categorical_crossentropy: 0.4570\n","Epoch 7/20\n","300/300 [==============================] - ETA: 0s - loss: 0.9394 - accuracy: 0.6867 - categorical_crossentropy: 0.9393\n","Epoch 7: val_accuracy improved from 0.85730 to 0.86190, saving model to /content/drive/MyDrive/Colab Notebooks/SIFT+GAN handwriting/GAN_SIFTNET_EMNIST_best_weights.hdf5\n","300/300 [==============================] - 71s 237ms/step - loss: 0.9394 - accuracy: 0.6867 - categorical_crossentropy: 0.9393 - val_loss: 0.4291 - val_accuracy: 0.8619 - val_categorical_crossentropy: 0.4289\n","Epoch 8/20\n","300/300 [==============================] - ETA: 0s - loss: 0.9084 - accuracy: 0.6961 - categorical_crossentropy: 0.9082\n","Epoch 8: val_accuracy improved from 0.86190 to 0.87580, saving model to /content/drive/MyDrive/Colab Notebooks/SIFT+GAN handwriting/GAN_SIFTNET_EMNIST_best_weights.hdf5\n","300/300 [==============================] - 69s 230ms/step - loss: 0.9084 - accuracy: 0.6961 - categorical_crossentropy: 0.9082 - val_loss: 0.4017 - val_accuracy: 0.8758 - val_categorical_crossentropy: 0.4015\n","Epoch 9/20\n","300/300 [==============================] - ETA: 0s - loss: 0.8972 - accuracy: 0.7005 - categorical_crossentropy: 0.8971\n","Epoch 9: val_accuracy did not improve from 0.87580\n","300/300 [==============================] - 68s 227ms/step - loss: 0.8972 - accuracy: 0.7005 - categorical_crossentropy: 0.8971 - val_loss: 0.4162 - val_accuracy: 0.8751 - val_categorical_crossentropy: 0.4160\n","Epoch 10/20\n","300/300 [==============================] - ETA: 0s - loss: 0.8760 - accuracy: 0.7057 - categorical_crossentropy: 0.8759\n","Epoch 10: val_accuracy did not improve from 0.87580\n","300/300 [==============================] - 68s 227ms/step - loss: 0.8760 - accuracy: 0.7057 - categorical_crossentropy: 0.8759 - val_loss: 0.4070 - val_accuracy: 0.8733 - val_categorical_crossentropy: 0.4069\n","Epoch 11/20\n","300/300 [==============================] - ETA: 0s - loss: 0.8694 - accuracy: 0.7075 - categorical_crossentropy: 0.8693\n","Epoch 11: val_accuracy did not improve from 0.87580\n","300/300 [==============================] - 70s 235ms/step - loss: 0.8694 - accuracy: 0.7075 - categorical_crossentropy: 0.8693 - val_loss: 0.4146 - val_accuracy: 0.8710 - val_categorical_crossentropy: 0.4145\n","Epoch 12/20\n","300/300 [==============================] - ETA: 0s - loss: 0.8481 - accuracy: 0.7157 - categorical_crossentropy: 0.8480\n","Epoch 12: val_accuracy improved from 0.87580 to 0.88250, saving model to /content/drive/MyDrive/Colab Notebooks/SIFT+GAN handwriting/GAN_SIFTNET_EMNIST_best_weights.hdf5\n","300/300 [==============================] - 69s 231ms/step - loss: 0.8481 - accuracy: 0.7157 - categorical_crossentropy: 0.8480 - val_loss: 0.3832 - val_accuracy: 0.8825 - val_categorical_crossentropy: 0.3831\n","Epoch 13/20\n","300/300 [==============================] - ETA: 0s - loss: 0.8369 - accuracy: 0.7159 - categorical_crossentropy: 0.8368\n","Epoch 13: val_accuracy did not improve from 0.88250\n","300/300 [==============================] - 68s 228ms/step - loss: 0.8369 - accuracy: 0.7159 - categorical_crossentropy: 0.8368 - val_loss: 0.3992 - val_accuracy: 0.8791 - val_categorical_crossentropy: 0.3991\n","Epoch 14/20\n","300/300 [==============================] - ETA: 0s - loss: 0.8346 - accuracy: 0.7186 - categorical_crossentropy: 0.8345\n","Epoch 14: val_accuracy did not improve from 0.88250\n","300/300 [==============================] - 68s 227ms/step - loss: 0.8346 - accuracy: 0.7186 - categorical_crossentropy: 0.8345 - val_loss: 0.3864 - val_accuracy: 0.8781 - val_categorical_crossentropy: 0.3863\n","Epoch 15/20\n","300/300 [==============================] - ETA: 0s - loss: 0.8184 - accuracy: 0.7217 - categorical_crossentropy: 0.8183\n","Epoch 15: val_accuracy did not improve from 0.88250\n","300/300 [==============================] - 70s 233ms/step - loss: 0.8184 - accuracy: 0.7217 - categorical_crossentropy: 0.8183 - val_loss: 0.3825 - val_accuracy: 0.8814 - val_categorical_crossentropy: 0.3823\n","Epoch 16/20\n","300/300 [==============================] - ETA: 0s - loss: 0.8182 - accuracy: 0.7264 - categorical_crossentropy: 0.8181\n","Epoch 16: val_accuracy improved from 0.88250 to 0.88300, saving model to /content/drive/MyDrive/Colab Notebooks/SIFT+GAN handwriting/GAN_SIFTNET_EMNIST_best_weights.hdf5\n","300/300 [==============================] - 68s 228ms/step - loss: 0.8182 - accuracy: 0.7264 - categorical_crossentropy: 0.8181 - val_loss: 0.3871 - val_accuracy: 0.8830 - val_categorical_crossentropy: 0.3870\n","Epoch 17/20\n","300/300 [==============================] - ETA: 0s - loss: 0.8095 - accuracy: 0.7274 - categorical_crossentropy: 0.8093\n","Epoch 17: val_accuracy did not improve from 0.88300\n","300/300 [==============================] - 68s 227ms/step - loss: 0.8095 - accuracy: 0.7274 - categorical_crossentropy: 0.8093 - val_loss: 0.3756 - val_accuracy: 0.8774 - val_categorical_crossentropy: 0.3754\n","Epoch 18/20\n","300/300 [==============================] - ETA: 0s - loss: 0.7989 - accuracy: 0.7269 - categorical_crossentropy: 0.7987\n","Epoch 18: val_accuracy improved from 0.88300 to 0.88430, saving model to /content/drive/MyDrive/Colab Notebooks/SIFT+GAN handwriting/GAN_SIFTNET_EMNIST_best_weights.hdf5\n","300/300 [==============================] - 69s 231ms/step - loss: 0.7989 - accuracy: 0.7269 - categorical_crossentropy: 0.7987 - val_loss: 0.3774 - val_accuracy: 0.8843 - val_categorical_crossentropy: 0.3772\n","Epoch 19/20\n","300/300 [==============================] - ETA: 0s - loss: 0.8051 - accuracy: 0.7297 - categorical_crossentropy: 0.8050\n","Epoch 19: val_accuracy did not improve from 0.88430\n","300/300 [==============================] - 78s 259ms/step - loss: 0.8051 - accuracy: 0.7297 - categorical_crossentropy: 0.8050 - val_loss: 0.3841 - val_accuracy: 0.8841 - val_categorical_crossentropy: 0.3840\n","Epoch 20/20\n","300/300 [==============================] - ETA: 0s - loss: 0.7889 - accuracy: 0.7347 - categorical_crossentropy: 0.7887\n","Epoch 20: val_accuracy improved from 0.88430 to 0.89020, saving model to /content/drive/MyDrive/Colab Notebooks/SIFT+GAN handwriting/GAN_SIFTNET_EMNIST_best_weights.hdf5\n","300/300 [==============================] - 76s 252ms/step - loss: 0.7889 - accuracy: 0.7347 - categorical_crossentropy: 0.7887 - val_loss: 0.3658 - val_accuracy: 0.8902 - val_categorical_crossentropy: 0.3657\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":1669829516792,"user_tz":-330,"elapsed":35,"user":{"displayName":"ROHAN DUBEY","userId":"11200230061670192260"}},"outputId":"f39efff7-8e37-4b71-e43f-3bce0f436197"},"execution_count":25,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 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2FfrBdBa5WRk6Qu+DdpsIzOws4Al3j6YBq0gaqm9qYe2muoTCvo7VFbWsqayloXnnn9qg/Gw+NaSAUw8dzrjB+WRnZmCAmWEGBmAWzgNj5/zW6fAfGWbk58QoyMkiPydGYViPnZ8TIz87k4wMFeDpKpkzgrnAT83sUeBud1+V7MbN7FTgZ0AM+LW7395u+U+Ak8LJPGCouw9IdvsiPck9aIVS39RCfVOc+qYWGppbh8G81mHrOonTtQ3NbYX/+s3baa1lMYPRA/tx0JACjjtoMJ8aWsBBQws4aEgBg/KzU/uhJS3sNhG4+z+bWRFwEXCvmTlwD/B7d6/p7H1mFgPuBE4BSoHXzewxd1+RsO3rEta/Gpix159EZC+5O1u3N1FeU0/5tgbKt9VTsW3neHk4vqm2Ybd15F3plxXjgMF5HDqqP587bBQHDS3gU0MKGF+cT7/snmsqKNJeUtcI3H2bmT0C9AOuBc4Fvmlmd7j7zzt525HAandfA2BmDwLnACs6Wf8i4NY9CV6kK43NcTbXNVJV10BVbTDcWdCHhXxY+Dc2f7Lmc0BeFsMKcxnWP5eDhxVSXJhDfnaMnMwYuVkZ5GTFyM2KkZOZQW5WjNzWYeK8rGCYHctQ1Yv0WslcIzgbuBT4FHAfcKS7V5hZHkGh3lkiGAWsT5guBY7qZB8HAOOB55MPXdJNc0uczdsbqaptZHNd0NZ8c11jWyEfDHcuq6lv7nA7+dkxhvXPZVhhLjPHDmRYUS5Di3IZXpTLsKIchhXl9vgNPSKplMwZwfnAT9z9xcSZ7r7dzC7rpjguBB5x9w4bGZvZPGAewNix3d/PhvQu7k5ZdT0ry7aFrxpWlm1jbVVdW1PIRBkGg/JzGJyfzeCCbKaMLKK4IGiWOLggm8H52cHygmyGFeVSkKPGciKJkvmLmA+UtU6YWT9gmLuvc/fnunjfBmBMwvTocF5HLgS+0dmG3H0BsACgpKRk7ytppdepb2phdUUtK9oK/W2s+riGrQlNJccOymPSiELOnDaCIUW5FOdnJxTyOfTvl6VqF5F9kEwieBg4NmG6JZx3xG7e9zowwczGEySAC4EvtF/JzCYCA4GXkwlY+qZ43NlU28DKj2sSjvS38UFlXVv/LLlZGUwcXsRph45g8ohCJo0o4pDhhRTmZqU4epH9WzKJINPdG1sn3L3RzHbbps3dm83sKuBpguajd7v7O2Z2G7DY3R8LV70QeNC9o5N+6Y3cne2NLWwO6+M3b29kSzi+ZXsjm+ua2FzXwJa6prZlW7Y3ktjgZkT/XCaNKOKUycOYNKKISSOKGDc4v61zLxHpOckkgkozO7u14Dazc4BNyWzc3Z8Enmw375Z20/OTC1V6UkNzC+s2bef9ihreLw9uelq7qa6t4O+olQ0EvTYOzMtmUH4WA/OymTA0aAs/KD+oqz94eCGThhcxUO3jRXqNZBLBlcD9ZvbfBDcorge+GGlU0mPqm1ra7m59v7w2KPgravmwantblY1ZUE9/YHE+U0YWMSg/m4Fh4T4ob9fxwlzdoSrS1yRzQ9kHwNFmVhBO10YelXS75pY4K8tq2gr64Ci/ho8S7nCNZRgHDM5jwtACTj90BBOGFQR3uQ4pUFNKkf1YUu3ozOwMYAqQ29qhlLvfFmFc0g2qtzfxt/creW5lOS+8W0n1jqAlTlbMGF+cz5SR/TnnsFFMGFbAhKGFjCvOIydTBb5IuknmhrL/IegH6CTg18Dngdcijkv20prKWp5fVcGzK8t5fd0WWuLOoPxsPj1pGCceMoTJI4o4YHCeuv0VkTbJnBEc6+7TzGyZu/+7mf0Y+EvUgUlymlriLF63hedXlfPcygrWbKoD4JBhhXz1hAOZPWkYh40ZoNY4ItKpZBJBfTjcbmYjgSpgRHQhye5Ub2/ihfcqeG5lBS+8W8G2+mayYxkcfdBgvnTsOE6eOJQxg/JSHaaI9BHJJILHzWwA8EPgDYIHH90VaVTyCVu3N/Lw4lKeXVnO4g+DKp/igmw+O2U4sycN4/gJxeo6QUT2Spclh5llAM+5+1bgUTNbCOS6e3WPRCe4O4++sYHvP7mSqrpGJo0o4msnHsTsSUOZPnqAmmqKyD7rMhG4e9zM7iR8ToC7NwANPRGYwPvlNdz8p+W8unYzh48dwH2XHcmUkf1THZaI7GeSqUt4zszOB/6fuoHoGTsaW7jj+fe568U15Odk8v3zpjK3ZIyO/kUkEskkgq8C1wPNZlZPcHexu3tRpJGlqedWlnPrY+9QumUHn585mhtPm8jggpxUhyUi+7Fk7iwu7IlA0t2GrTv498fe4a8rypkwtICH5h3NUQcOTnVYIpIGkrmh7ISO5rd/UI3snaaWOPf8Yy0/ffZ94u58+9SJXHb8eLIzdcOXiPSMZKqGvpkwnkvwLOIlwMmRRJRGFq/bzM1/Ws6qj2uYPXEo88+eovb/ItLjkqkaOitx2szGAD+NLKI0sKWukdv/soqHFq9nZP9cfnXJTD4zeRit/TiJiPSkvbkDqRSY1N2BpAN35+ElpXz/yZVsq2/mqyccyL/MnkC+bgQTkRRK5hrBzwnuJgbIAA4juMNY9sC6TXV865FlvLZuMyUHDOR75x7KxOFqeCUiqZfMoejihPFm4Pfu/o+I4tkvPb+qnGseXEqGGT84fxqfnzla9wSISK+RTCJ4BKh39xYAM4uZWZ67b482tL4vHnd+/vxqfvrce0waXsSvLpmpi8Ei0usk00bxOaBfwnQ/4Nlowtl/bKtvYt5vl/CTZ9/jc4eN4tGvHaskICK9UjJnBLmJj6d091ozU4nWhdUVNcz77RI+rNrO/LMm86Vjx6lFkIj0WskkgjozO9zd3wAws5nAjmjD6rueWv4x//qHpfTLjnH/5UdxtO4OFpFeLplEcC3wsJltJOhnaDgwN5mNm9mpwM+AGPBrd7+9g3UuAOYTtEx6y92/kFzovUtL3PnJM+/x34tWM33MAP7nnw9nRP9+u3+jiEiKJXND2etmNhE4JJz1rrs37e59ZhYD7gROIbj34HUze8zdVySsMwG4ETjO3beY2dC9+RCpVr29iX958E3+9l4lFx4xhvlnTyE3Sw+BF5G+YbcXi83sG0C+uy939+VAgZl9PYltHwmsdvc17t4IPAic026dK4A73X0LgLtX7Fn4qbeybBtn/ff/8tIHm/iPc6dy+/nTlAREpE9JptXQFeETygAIC+0rknjfKGB9wnRpOC/RwcDBZvYPM3slrErqMx5/ayPn/eIl6ptaeHDeMXzhqLGpDklEZI8lc40gZmbW+lCasMonuxv3PwGYBYwGXjSzqYmJJ9znPGAewNixqS9sm1vi/ODpd1nw4hpKDhjILy4+nKFFuakOS0RkrySTCJ4CHjKzX4XTXwX+ksT7NgBjEqZHh/MSlQKvhtcc1prZewSJ4fXEldx9AbAAoKSkJKVPSdtc18jVv3+Df6yu4ovHHMDNZ0xWl9Ei0qclkwi+TXA0fmU4vYyg5dDuvA5MMLPxBAngQqB9i6A/ARcB95hZMUFV0Zoktp0SyzdU89XfLqGytoEffn4ac0rG7P5NIiK93G4PZd09DrwKrCO4AHwysDKJ9zUDVwFPh+v/wd3fMbPbzOzscLWngSozWwEsAr7p7lV780Gi9trazZz/y5dwdx658hglARHZb3R6RmBmBxMcrV8EbAIeAnD3k5LduLs/CTzZbt4tCeNO8Dzk6/co6hS496W1FOZm8vjVx+sZwiKyX+mqamgV8HfgTHdfDWBm1/VIVL1MXUMzz6+q4IKSMUoCIrLf6apq6DygDFhkZneZ2WyCO4vTzrMry6lvinPG1BGpDkVEpNt1mgjc/U/ufiEwkaD+/lpgqJn90sw+01MB9gZPLCtjWFEOR4wblOpQRES6XTIXi+vc/YHw2cWjgTcJWhKlhZr6Jl54r5LTp47Qw2REZL+0Rw3g3X2Luy9w99lRBdTbPLOinMbmOGdOG5nqUEREIqE7oXbjiWVljOyfy4wxA1IdiohIJJQIulC9vYkX36/kjGmqFhKR/ZcSQReeXvExTS2uaiER2a8pEXRh4bIyxgzqx7TR/VMdiohIZJQIOrGlrpF/rN7EGVNH6nnDIrJfUyLoxFPvfExL3Dlzmm4iE5H9mxJBJxYu28j44nymjCxKdSgiIpFSIujAptoGXv6gijOmjlC1kIjs95QIOvCX5R8TdzhzuqqFRGT/p0TQgYVvbeRTQws4ZFhhqkMREYmcEkE7FdvqeW3dZlULiUjaUCJo58m3y3CHs1QtJCJpQomgnYXLypg4vJBPDVW1kIikByWCBGXVO1j84RbdOyAiaUWJIMETy8oAOEN9C4lIGlEiSLBwWRlTRhYxvjg/1aGIiPQYJYLQ+s3bWbp+q3oaFZG0o0QQevLtsFpID6gXkTQTaSIws1PN7F0zW21mN3Sw/MtmVmlmS8PX5VHG05WFy8qYPro/YwfnpSoEEZGUiCwRmFkMuBM4DZgMXGRmkztY9SF3Pyx8/TqqeLqyblMdb2+oVrWQiKSlKM8IjgRWu/sad28EHgTOiXB/e+2JsFrodDUbFZE0FGUiGAWsT5guDee1d76ZLTOzR8xsTEcbMrN5ZrbYzBZXVlZ2e6ALl5Vx+NgBjBrQr9u3LSLS26X6YvHjwDh3nwY8A/ymo5XcfYG7l7h7yZAhQ7o1gA8qa1lZtk3VQiKStqJMBBuAxCP80eG8Nu5e5e4N4eSvgZkRxtOhJ5aVYQanq7WQiKSpKBPB68AEMxtvZtnAhcBjiSuYWWLpezawMsJ4OrRw2UaOOGAQw/vn9vSuRUR6hcgSgbs3A1cBTxMU8H9w93fM7DYzOztc7V/M7B0zewv4F+DLUcXTkffKa3ivvFYPoBGRtJYZ5cbd/UngyXbzbkkYvxG4McoYurJwWRkZBqcdqkQgIukr1ReLU8bdWbhsI0cfOJghhTmpDkdEJGXSNhGsLKthTWUdZ+jeARFJc2mbCBYu20gsw1QtJCJpLy0TgbvzxNtlHHvQYAblZ6c6HBGRlErLRLB8wzY+rNquJ5GJiJCmiWDhso1kZhifnTI81aGIiKRc2iWCoLVQGf80oZgBeaoWEhFJu0SwdP1WNmzdob6FRERCaZcIFi4rIzuWwSlThqU6FBGRXiGtEkE87jz5dhknHDyEotysVIcjItIrpFUieOOjLZRV13OW+hYSEWmTVolg4bIycjIzmD1J1UIiIq3SJhG0hNVCJx0ylIKcSPvaExHpU9ImEby+bjMVNQ3qclpEpJ20SQRL128lLzvGyROHpjoUEZFeJW3qSK488SAuOmIsedlp85FFRJKSNmcEAP3z1GRURKQ9HR6LyD5pamqitLSU+vr6VIciQG5uLqNHjyYrK/kDXyUCEdknpaWlFBYWMm7cOMws1eGkNXenqqqK0tJSxo8fn/T70qpqSES6X319PYMHD1YS6AXMjMGDB+/x2ZkSgYjsMyWB3mNv/i+UCERE0lykicDMTjWzd81stZnd0MV655uZm1lJlPGIiMgnRZYIzCwG3AmcBkwGLjKzyR2sVwhcA7waVSwiIt2hubk51SFEIspWQ0cCq919DYCZPQicA6xot953gf8EvhlhLCLSA/798XdYsXFbt25z8sgibj1rym7X+9znPsf69eupr6/nmmuuYd68eTz11FPcdNNNtLS0UFxczHPPPUdtbS1XX301ixcvxsy49dZbOf/88ykoKKC2thaARx55hIULF3Lvvffy5S9/mdzcXN58802OO+44LrzwQq655hrq6+vp168f99xzD4cccggtLS18+9vf5qmnniIjI4MrrriCKVOmcMcdd/CnP/0JgGeeeYZf/OIX/PGPf+zW72hfRZkIRgHrE6ZLgaMSVzCzw4Ex7v6EmXWaCMxsHjAPYOzYsRGEKiJ93d13382gQYPYsWMHRxxxBOeccw5XXHEFL774IuPHj2fz5s0AfPe736V///68/fbbAGzZsmW32y4tLeWll14iFouxbds2/v73v5OZmcmzzz7LTTfdxKOPPsqCBQtYt24dS5cuJTMzk82bNzNw4EC+/vWvU1lZyZAhQ7jnnnv4yle+Eun3sDdSdh+BmWUA/wV8eXfruvsCYAFASUmJRxuZiOytZI7co3LHHXx5UbEAAA32SURBVHe0HWmvX7+eBQsWcMIJJ7S1px80aBAAzz77LA8++GDb+wYOHLjbbc+ZM4dYLAZAdXU1X/rSl3j//fcxM5qamtq2e+WVV5KZmbnL/i655BJ+97vfcemll/Lyyy9z3333ddMn7j5RJoINwJiE6dHhvFaFwKHAC2Fzp+HAY2Z2trsvjjAuEdnPvPDCCzz77LO8/PLL5OXlMWvWLA477DBWrVqV9DYSm122b4efn5/fNv5v//ZvnHTSSfzxj39k3bp1zJo1q8vtXnrppZx11lnk5uYyZ86ctkTRm0TZauh1YIKZjTezbOBC4LHWhe5e7e7F7j7O3ccBrwBKAiKyx6qrqxk4cCB5eXmsWrWKV155hfr6el588UXWrl0L0FY1dMopp3DnnXe2vbe1amjYsGGsXLmSeDzeZR1+dXU1o0aNAuDee+9tm3/KKafwq1/9qu2Ccuv+Ro4cyciRI/ne977HpZde2n0fuhtFlgjcvRm4CngaWAn8wd3fMbPbzOzsqPYrIunn1FNPpbm5mUmTJnHDDTdw9NFHM2TIEBYsWMB5553H9OnTmTt3LgA333wzW7Zs4dBDD2X69OksWrQIgNtvv50zzzyTY489lhEjOn9uybe+9S1uvPFGZsyYsUsrossvv5yxY8cybdo0pk+fzgMPPNC27OKLL2bMmDFMmjQpom9g35h736pyLykp8cWLddIg0lusXLmy1xZwvcVVV13FjBkzuOyyy3pkfx39n5jZEnfv8F6t3ldZJSKyH5k5cyb5+fn8+Mc/TnUonVIiEBGJ0JIlS1Idwm6pryERkTSnRCAikuaUCERE0pwSgYhImlMiEBFJc0oEIpJWCgoKUh1Cr6PmoyLSff5yA3z8dvduc/hUOO327t1mL9Dc3Nxr+h3SGYGI9Gk33HDDLn0HzZ8/n+9973vMnj2bww8/nKlTp/LnP/85qW3V1tZ2+r777ruvrfuISy65BIDy8nLOPfdcpk+fzvTp03nppZdYt24dhx56aNv7fvSjHzF//nwAZs2axbXXXktJSQk/+9nPePzxxznqqKOYMWMGn/70pykvL2+L49JLL2Xq1KlMmzaNRx99lLvvvptrr722bbt33XUX11133V5/b7tw9z71mjlzpotI77FixYqU7v+NN97wE044oW160qRJ/tFHH3l1dbW7u1dWVvpBBx3k8Xjc3d3z8/M73VZTU1OH71u+fLlPmDDBKysr3d29qqrK3d0vuOAC/8lPfuLu7s3Nzb5161Zfu3atT5kypW2bP/zhD/3WW291d/cTTzzRv/a1r7Ut27x5c1tcd911l19//fXu7v6tb33Lr7nmml3Wq6mp8QMPPNAbGxvd3f2YY47xZcuWdfg5Ovo/ARZ7J+Vq7zgvERHZSzNmzKCiooKNGzdSWVnJwIEDGT58ONdddx0vvvgiGRkZbNiwgfLycoYPH97lttydm2666RPve/7555kzZw7FxcXAzmcNPP/8823PF4jFYvTv33+3D7pp7fwOggfezJ07l7KyMhobG9uendDZMxNOPvlkFi5cyKRJk2hqamLq1Kl7+G11TIlARPq8OXPm8Mgjj/Dxxx8zd+5c7r//fiorK1myZAlZWVmMGzfuE88Y6Mjevi9RZmYm8Xi8bbqrZxtcffXVXH/99Zx99tm88MILbVVInbn88sv5j//4DyZOnNitXVrrGoGI9Hlz587lwQcf5JFHHmHOnDlUV1czdOhQsrKyWLRoER9++GFS2+nsfSeffDIPP/wwVVVVwM5nDcyePZtf/vKXALS0tFBdXc2wYcOoqKigqqqKhoYGFi5c2OX+Wp9t8Jvf/KZtfmfPTDjqqKNYv349DzzwABdddFGyX89uKRGISJ83ZcoUampqGDVqFCNGjODiiy9m8eLFTJ06lfvuu4+JEycmtZ3O3jdlyhS+853vcOKJJzJ9+nSuv/56AH72s5+xaNEipk6dysyZM1mxYgVZWVnccsstHHnkkZxyyild7nv+/PnMmTOHmTNntlU7QefPTAC44IILOO6445J6xGay9DwCEdkneh5BzzrzzDO57rrrmD17dqfr7OnzCHRGICLSB2zdupWDDz6Yfv36dZkE9oYuFotI2nn77bfb7gVolZOTw6uvvpqiiHZvwIABvPfee5FsW4lARPaZu2NmqQ4jaVOnTmXp0qWpDiMSe1Pdr6ohEdknubm5VFVV7VUBJN3L3amqqiI3N3eP3qczAhHZJ6NHj6a0tJTKyspUhyIEiXn06NF79B4lAhHZJ1lZWW13xErfFGnVkJmdambvmtlqM7uhg+VXmtnbZrbUzP7XzCZHGY+IiHxSZInAzGLAncBpwGTgog4K+gfcfaq7Hwb8APivqOIREZGORXlGcCSw2t3XuHsj8CBwTuIK7r4tYTIf0NUmEZEeFuU1glHA+oTpUuCo9iuZ2TeA64Fs4OSONmRm84B54WStmb27lzEVA5v28r09QfHtG8W373p7jIpv7x3Q2YKUXyx29zuBO83sC8DNwJc6WGcBsGBf92Vmizu7xbo3UHz7RvHtu94eo+KLRpRVQxuAMQnTo8N5nXkQ+FyE8YiISAeiTASvAxPMbLyZZQMXAo8lrmBmExImzwDejzAeERHpQGRVQ+7ebGZXAU8DMeBud3/HzG4jeGTaY8BVZvZpoAnYQgfVQt1sn6uXIqb49o3i23e9PUbFF4E+1w21iIh0L/U1JCKS5pQIRETS3H6ZCJLo2iLHzB4Kl79qZuN6MLYxZrbIzFaY2Ttmdk0H68wys+qw642lZnZLT8UX7n9dQtcfn3gcnAXuCL+/ZWZ2eA/GdkjC97LUzLaZ2bXt1unx78/M7jazCjNbnjBvkJk9Y2bvh8MOny1oZl8K13nfzLr9Olknsf3QzFaF/39/NLMBnby3y99CxDHON7MNCf+Pp3fy3i7/3iOM76GE2NaZWYf9WvfUd7hP3H2/ehFcmP4AOJDgJrW3gMnt1vk68D/h+IXAQz0Y3wjg8HC8EHivg/hmAQtT+B2uA4q7WH468BfAgKOBV1P4f/0xcECqvz/gBOBwYHnCvB8AN4TjNwD/2cH7BgFrwuHAcHxgD8T2GSAzHP/PjmJL5rcQcYzzgf+TxG+gy7/3qOJrt/zHwC2p/A735bU/nhHstmuLcPo34fgjwGzroadquHuZu78RjtcAKwnuwu5LzgHu88ArwAAzG5GCOGYDH7j7hynY9y7c/UVgc7vZib+z39DxfTKfBZ5x983uvgV4Bjg16tjc/a/u3hxOvkJwn0/KdPL9JSOZv/d91lV8YdlxAfD77t5vT9kfE0FHXVu0L2jb1gn/GKqBwT0SXYKwSmoG0NHz8Y4xs7fM7C9mNqVHAwv6fPqrmS0Ju/doL5nvuCdcSOd/fKn8/loNc/eycPxjYFgH6/SG7/IrBGd4HdndbyFqV4XVV3d3UrXWG76/fwLK3b2z+6BS/R3u1v6YCPoEMysAHgWu9V073wN4g6C6Yzrwc+BPPRze8e5+OEHPsd8wsxN6eP+7Fd6keDbwcAeLU/39fYIHdQS9rq22mX0HaAbu72SVVP4WfgkcBBwGlBFUv/RGF9H12UCv/3vaHxNBMl1btK1jZplAf6CqR6IL9plFkATud/f/1365u29z99pw/Ekgy8yKeyo+d98QDiuAPxKcfifa0+5DonAa8Ia7l7dfkOrvL0F5a5VZOKzoYJ2UfZdm9mXgTODiMFF9QhK/hci4e7m7t7h7HLirk32n9LcYlh/nAQ91tk4qv8Nk7Y+JYLddW4TTra0zPg8839kfQncL6xP/L7DS3Tt8/oKZDW+9ZmFmRxL8P/VIojKzfDMrbB0nuKi4vN1qjwFfDFsPHQ1UJ1SB9JROj8JS+f21k/g7+xLw5w7WeRr4jJkNDKs+PhPOi5SZnQp8Czjb3bd3sk4yv4UoY0y87nRuJ/tO5u89Sp8GVrl7aUcLU/0dJi3VV6ujeBG0anmPoDXBd8J5txH86AFyCaoUVgOvAQf2YGzHE1QRLAOWhq/TgSuBK8N1rgLeIWgB8QpwbA/Gd2C437fCGFq/v8T4jOChQx8AbwMlPfz/m09QsPdPmJfS748gKZURdJdSClxGcN3pOYI+tJ4FBoXrlgC/TnjvV8Lf4mrg0h6KbTVB3Xrrb7C1Fd1I4Mmufgs9+P39Nvx9LSMo3Ee0jzGc/sTfe0/EF86/t/V3l7BuSr7DfXmpiwkRkTS3P1YNiYjIHlAiEBFJc0oEIiJpTolARCTNKRGIiKQ5JQKRdsyspV0Pp93Wo6WZjUvswVKkN4jsUZUifdgOdz8s1UGI9BSdEYgkKexX/gdh3/KvmdmnwvnjzOz5sHO058xsbDh/WNjX/1vh69hwUzEzu8uC51H81cz6pexDiaBEINKRfu2qhuYmLKt296nAfwM/Def9HPiNu08j6LztjnD+HcDfPOj87nCCO0sBJgB3uvsUYCtwfsSfR6RLurNYpB0zq3X3gg7mrwNOdvc1YceBH7v7YDPbRND9QVM4v8zdi82sEhjt7g0J2xhH8PyBCeH0t4Esd/9e9J9MpGM6IxDZM97J+J5oSBhvQdfqJMWUCET2zNyE4cvh+EsEvV4CXAz8PRx/DvgagJnFzKx/TwUpsid0JCLySf3aPYj8KXdvbUI60MyWERzVXxTOuxq4x8y+CVQCl4bzrwEWmNllBEf+XyPowVKkV9E1ApEkhdcIStx9U6pjEelOqhoSEUlzOiMQEUlzOiMQEUlzSgQiImlOiUBEJM0pEYiIpDklAhGRNPf/AQIaarXamL1lAAAAAElFTkSuQmCC\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":1669829518865,"user_tz":-330,"elapsed":2079,"user":{"displayName":"ROHAN DUBEY","userId":"11200230061670192260"}},"outputId":"9dd7b1e0-e23c-4b28-ba82-1ee82057379f"},"execution_count":26,"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":{"id":"_iIDnrQlQDGY","executionInfo":{"status":"aborted","timestamp":1669828061428,"user_tz":-330,"elapsed":792333,"user":{"displayName":"ROHAN DUBEY","userId":"11200230061670192260"}}},"execution_count":null,"outputs":[]},{"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(0,27).astype('str'), prediction[0,:])\n","plt.show()"],"metadata":{"id":"R1K4JKmEoVx7","executionInfo":{"status":"aborted","timestamp":1669828061425,"user_tz":-330,"elapsed":16,"user":{"displayName":"ROHAN DUBEY","userId":"11200230061670192260"}}},"execution_count":null,"outputs":[]},{"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","executionInfo":{"status":"aborted","timestamp":1669828061426,"user_tz":-330,"elapsed":830799,"user":{"displayName":"ROHAN DUBEY","userId":"11200230061670192260"}}},"execution_count":null,"outputs":[]},{"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","executionInfo":{"status":"aborted","timestamp":1669828061426,"user_tz":-330,"elapsed":822896,"user":{"displayName":"ROHAN DUBEY","userId":"11200230061670192260"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["from sklearn.metrics import confusion_matrix\n","confusion_mat = confusion_matrix(true_labels, pred_labels)\n","confusion_mat"],"metadata":{"id":"JZuxHfnFQxgA","executionInfo":{"status":"aborted","timestamp":1669828061427,"user_tz":-330,"elapsed":822061,"user":{"displayName":"ROHAN DUBEY","userId":"11200230061670192260"}}},"execution_count":null,"outputs":[]},{"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(0,26).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","executionInfo":{"status":"aborted","timestamp":1669828061427,"user_tz":-330,"elapsed":812871,"user":{"displayName":"ROHAN DUBEY","userId":"11200230061670192260"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"5FqM32UOoucd"},"execution_count":null,"outputs":[]}],"metadata":{"colab":{"provenance":[],"authorship_tag":"ABX9TyO2zN1/f+ELQ0Kk+FPSZLLh"},"gpuClass":"standard","kernelspec":{"display_name":"Python 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