Transfer Learning with AttentionBasedVGG16/Fine Tuning with Attention Based pre-Trained VGG16 for Tuberculosis Classification.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"Fine Tuning with Attention Based pre-Trained VGG16 for Tuberculosis Classification.ipynb","provenance":[],"collapsed_sections":[],"authorship_tag":"ABX9TyPnYBYE4QK3WVTdJylTAl4g"},"kernelspec":{"name":"python3","display_name":"Python 3"},"accelerator":"GPU"},"cells":[{"cell_type":"code","metadata":{"id":"BYi3VGTuVmIU","colab_type":"code","outputId":"8d67a9cb-44e3-4d36-8f7f-def2c388ab29","executionInfo":{"status":"ok","timestamp":1582539996446,"user_tz":-300,"elapsed":68480,"user":{"displayName":"ZEESHAN NISAR","photoUrl":"","userId":"05353617243036990298"}},"colab":{"base_uri":"https://localhost:8080/","height":122}},"source":["from google.colab import drive\n","drive.mount('/content/drive')"],"execution_count":1,"outputs":[{"output_type":"stream","text":["Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n","\n","Enter your authorization code:\n","··········\n","Mounted at /content/drive\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"88b1g_shV1Mg","colab_type":"code","outputId":"80c2ee4f-709e-461c-dea4-c0d2e02ef1fc","executionInfo":{"status":"ok","timestamp":1582540271990,"user_tz":-300,"elapsed":10518,"user":{"displayName":"ZEESHAN NISAR","photoUrl":"","userId":"05353617243036990298"}},"colab":{"base_uri":"https://localhost:8080/","height":114}},"source":["%matplotlib inline\n","import os\n","import numpy as np\n","from keras.preprocessing.image import ImageDataGenerator\n","\n","os.chdir('/content/drive/My Drive/GitHub Repositories/Tuberculosis/Tuberculosis Classification')\n","\n","trainDir = './data/Train'\n","validDir = './data/Test'\n","\n","IMG_SIZE = 512 # slightly smaller than vgg16 normally expects\n","datagen = ImageDataGenerator(rescale=1./255, \n","                             samplewise_center=False, \n","                             samplewise_std_normalization=False, \n","                             horizontal_flip = True, \n","                             vertical_flip = False, \n","                             height_shift_range = 0.15, \n","                             width_shift_range = 0.15, \n","                             rotation_range = 5, \n","                             shear_range = 0.01,\n","                             fill_mode = 'nearest',\n","                             zoom_range=0.1,  \n","                             )\n","\n","train_gen = datagen.flow_from_directory(\n","    directory=trainDir,\n","    target_size=(IMG_SIZE, IMG_SIZE),\n","    color_mode=\"rgb\",\n","    batch_size=32,\n","    class_mode='categorical',\n","    shuffle=True,\n","    seed=2019)\n","\n","valid_gen = datagen.flow_from_directory(\n","    directory=validDir,\n","    target_size=(IMG_SIZE, IMG_SIZE),\n","    color_mode=\"rgb\",\n","    batch_size=32,\n","    class_mode='categorical',\n","    shuffle=False,\n","    seed=2019)\n","\n","\n"],"execution_count":2,"outputs":[{"output_type":"stream","text":["Using TensorFlow backend.\n"],"name":"stderr"},{"output_type":"display_data","data":{"text/html":["<p style=\"color: red;\">\n","The default version of TensorFlow in Colab will soon switch to TensorFlow 2.x.<br>\n","We recommend you <a href=\"https://www.tensorflow.org/guide/migrate\" target=\"_blank\">upgrade</a> now \n","or ensure your notebook will continue to use TensorFlow 1.x via the <code>%tensorflow_version 1.x</code> magic:\n","<a href=\"https://colab.research.google.com/notebooks/tensorflow_version.ipynb\" target=\"_blank\">more info</a>.</p>\n"],"text/plain":["<IPython.core.display.HTML object>"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["Found 721 images belonging to 2 classes.\n","Found 79 images belonging to 2 classes.\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"hvkFrvcYW5n-","colab_type":"code","outputId":"adfc16b9-663d-456d-9174-ac79da5cc58a","executionInfo":{"status":"error","timestamp":1582540326327,"user_tz":-300,"elapsed":52213,"user":{"displayName":"ZEESHAN NISAR","photoUrl":"","userId":"05353617243036990298"}},"colab":{"base_uri":"https://localhost:8080/","height":231}},"source":["t_x, t_y = next(train_gen)\n","fig, m_axs = plt.subplots(2, 4, figsize = (16, 8))\n","for (c_x, c_y, c_ax) in zip(t_x, t_y, m_axs.flatten()):\n","    c_ax.imshow(c_x[:,:,0], cmap = 'bone', vmin = 0, vmax = 1)\n","    c_ax.set_title('%s' % ('Pulmonary Abnormality' if np.argmax(c_y)>0.5 else 'Healthy'))\n","    c_ax.axis('off')"],"execution_count":3,"outputs":[{"output_type":"error","ename":"NameError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)","\u001b[0;32m<ipython-input-3-0753ac351d41>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mt_x\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mt_y\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_gen\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mm_axs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m 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\u001b[0mc_ax\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mc_x\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcmap\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'bone'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmin\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmax\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0mc_ax\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_title\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'%s'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m'Pulmonary Abnormality'\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mc_y\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m>\u001b[0m\u001b[0;36m0.5\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m'Healthy'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mNameError\u001b[0m: name 'plt' is not defined"]}]},{"cell_type":"code","metadata":{"id":"Hy5-D8xWXGbt","colab_type":"code","outputId":"0b4fbab3-fb7e-4117-fbc7-37bbdbede5c6","executionInfo":{"status":"ok","timestamp":1582540344507,"user_tz":-300,"elapsed":18154,"user":{"displayName":"ZEESHAN NISAR","photoUrl":"","userId":"05353617243036990298"}},"colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["from keras.applications.vgg16 import VGG16\n","from keras.layers import *\n","from keras.models import *\n","from keras.optimizers import *\n","\n","num_classes = 2\n","img_shape = (IMG_SIZE, IMG_SIZE, 3)\n","inputs = Input(img_shape)\n","\n","vgg16 = VGG16(input_shape =  img_shape, include_top = False, weights = 'imagenet')\n","vgg16.trainable = False\n","vgg16_depth = vgg16.get_output_shape_at(0)[-1]\n","vgg16_outputs = vgg16(inputs)\n","bn_outputs = BatchNormalization(name='BatchNormalization')(vgg16_outputs)\n","\n","# here we do an attention mechanism to turn pixels in the GAP on an off\n","atten_outputs = Conv2D(64, kernel_size = (1,1), padding = 'same', activation = 'relu')(bn_outputs)\n","atten_outputs = Conv2D(32, kernel_size = (1,1), padding = 'same', activation = 'relu')(atten_outputs)\n","atten_outputs = Conv2D(16, kernel_size = (1,1), padding = 'same', activation = 'relu')(atten_outputs)\n","atten_outputs = Conv2D(1, kernel_size = (1,1), padding = 'valid', activation = 'sigmoid')(atten_outputs)\n","\n","# fan it out to all of the channels\n","weights = np.ones((1, 1, 1, vgg16_depth))\n","outputs_up_c2 = Conv2D(vgg16_depth, kernel_size = (1,1), padding = 'same', activation = 'linear', use_bias = False, weights = [weights])\n","outputs_up_c2.trainable = False\n","atten_outputs = outputs_up_c2(atten_outputs)\n","\n","multiply_outputs = multiply([atten_outputs, bn_outputs], name='Multiply')\n","gap_features_ouputs = GlobalAveragePooling2D()(multiply_outputs)\n","gap_mask_ouputs = GlobalAveragePooling2D()(atten_outputs)\n","\n","# to account for missing values from the attention model\n","outputs = Lambda(lambda x: x[0]/x[1], name = 'RescaleGAP')([gap_features_ouputs, gap_mask_ouputs])\n","outputs = Dropout(0.5)(outputs)\n","outputs = Dense(128, activation = 'elu')(outputs)\n","outputs = Dropout(0.25)(outputs)\n","outputs = Dense(num_classes, activation = 'softmax')(outputs)\n","\n","model = Model(inputs = [inputs], outputs = [outputs])\n","model.compile(optimizer = 'adam', loss = 'categorical_crossentropy', metrics = ['accuracy'])\n","model.summary()"],"execution_count":4,"outputs":[{"output_type":"stream","text":["WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:66: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:541: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4432: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4267: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.\n","\n","Downloading data from https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5\n","58892288/58889256 [==============================] - 6s 0us/step\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:190: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:197: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:203: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:207: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:216: The name tf.is_variable_initialized is deprecated. Please use tf.compat.v1.is_variable_initialized instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:223: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:2041: The name tf.nn.fused_batch_norm is deprecated. Please use tf.compat.v1.nn.fused_batch_norm instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:148: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3733: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n","Instructions for updating:\n","Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/optimizers.py:793: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3576: The name tf.log is deprecated. Please use tf.math.log instead.\n","\n","Model: \"model_1\"\n","__________________________________________________________________________________________________\n","Layer (type)                    Output Shape         Param #     Connected to                     \n","==================================================================================================\n","input_1 (InputLayer)            (None, 512, 512, 3)  0                                            \n","__________________________________________________________________________________________________\n","vgg16 (Model)                   (None, 16, 16, 512)  14714688    input_1[0][0]                    \n","__________________________________________________________________________________________________\n","BatchNormalization (BatchNormal (None, 16, 16, 512)  2048        vgg16[1][0]                      \n","__________________________________________________________________________________________________\n","conv2d_1 (Conv2D)               (None, 16, 16, 64)   32832       BatchNormalization[0][0]         \n","__________________________________________________________________________________________________\n","conv2d_2 (Conv2D)               (None, 16, 16, 32)   2080        conv2d_1[0][0]                   \n","__________________________________________________________________________________________________\n","conv2d_3 (Conv2D)               (None, 16, 16, 16)   528         conv2d_2[0][0]                   \n","__________________________________________________________________________________________________\n","conv2d_4 (Conv2D)               (None, 16, 16, 1)    17          conv2d_3[0][0]                   \n","__________________________________________________________________________________________________\n","conv2d_5 (Conv2D)               (None, 16, 16, 512)  512         conv2d_4[0][0]                   \n","__________________________________________________________________________________________________\n","Multiply (Multiply)             (None, 16, 16, 512)  0           conv2d_5[0][0]                   \n","                                                                 BatchNormalization[0][0]         \n","__________________________________________________________________________________________________\n","global_average_pooling2d_1 (Glo (None, 512)          0           Multiply[0][0]                   \n","__________________________________________________________________________________________________\n","global_average_pooling2d_2 (Glo (None, 512)          0           conv2d_5[0][0]                   \n","__________________________________________________________________________________________________\n","RescaleGAP (Lambda)             (None, 512)          0           global_average_pooling2d_1[0][0] \n","                                                                 global_average_pooling2d_2[0][0] \n","__________________________________________________________________________________________________\n","dropout_1 (Dropout)             (None, 512)          0           RescaleGAP[0][0]                 \n","__________________________________________________________________________________________________\n","dense_1 (Dense)                 (None, 128)          65664       dropout_1[0][0]                  \n","__________________________________________________________________________________________________\n","dropout_2 (Dropout)             (None, 128)          0           dense_1[0][0]                    \n","__________________________________________________________________________________________________\n","dense_2 (Dense)                 (None, 2)            258         dropout_2[0][0]                  \n","==================================================================================================\n","Total params: 14,818,627\n","Trainable params: 102,403\n","Non-trainable params: 14,716,224\n","__________________________________________________________________________________________________\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"AhDfuHBzZeBh","colab_type":"code","outputId":"981c0ec5-bff9-4fd9-c3f1-6876a31313ff","colab":{"base_uri":"https://localhost:8080/","height":428}},"source":["from keras.callbacks import ModelCheckpoint, LearningRateScheduler, EarlyStopping, ReduceLROnPlateau\n","\n","weight_path=os.path.join( './Fine Tuning with AttentionBasedVGG16', '{}_pretrainedattentionbased_vgg16model.bestweights.hdf5'.format('TB'))\n","checkpoint = ModelCheckpoint(weight_path, monitor='val_acc', verbose=1, save_best_only=True, mode='auto', save_weights_only = True)\n","reduceLROnPlat = ReduceLROnPlateau(monitor='val_loss', factor=0.8, patience=10, verbose=1, mode='auto', epsilon=0.0001, cooldown=5, min_lr=0.0001)\n","early = EarlyStopping(monitor=\"'val_acc'\", mode=\"auto\", patience=30) \n","\n","callbacks_list = [checkpoint, early, reduceLROnPlat]\n","\n","STEP_SIZE_TRAIN=train_gen.n//train_gen.batch_size\n","STEP_SIZE_VALID=valid_gen.n//valid_gen.batch_size\n","model.fit_generator(train_gen, steps_per_epoch=STEP_SIZE_TRAIN, validation_data=valid_gen, validation_steps=STEP_SIZE_VALID,\n","                    epochs = 100, callbacks = callbacks_list)\n","\n"],"execution_count":0,"outputs":[{"output_type":"stream","text":["WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/math_grad.py:1424: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n","Instructions for updating:\n","Use tf.where in 2.0, which has the same broadcast rule as np.where\n"],"name":"stdout"},{"output_type":"stream","text":["/usr/local/lib/python3.6/dist-packages/keras/callbacks.py:1335: UserWarning: `epsilon` argument is deprecated and will be removed, use `min_delta` instead.\n","  warnings.warn('`epsilon` argument is deprecated and '\n"],"name":"stderr"},{"output_type":"stream","text":["WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1033: The name tf.assign_add is deprecated. Please use tf.compat.v1.assign_add instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1020: The name tf.assign is deprecated. Please use tf.compat.v1.assign instead.\n","\n","Epoch 1/100\n"],"name":"stdout"},{"output_type":"stream","text":["/usr/local/lib/python3.6/dist-packages/keras/utils/data_utils.py:610: UserWarning: The input 2 could not be retrieved. It could be because a worker has died.\n","  UserWarning)\n"],"name":"stderr"},{"output_type":"stream","text":[" 2/22 [=>............................] - ETA: 12:42 - loss: 0.5729 - acc: 0.6719"],"name":"stdout"},{"output_type":"stream","text":["/usr/local/lib/python3.6/dist-packages/keras/utils/data_utils.py:610: UserWarning: The input 14 could not be retrieved. It could be because a worker has died.\n","  UserWarning)\n"],"name":"stderr"},{"output_type":"stream","text":[" 4/22 [====>.........................] - ETA: 10:27 - loss: 0.6000 - acc: 0.6719"],"name":"stdout"},{"output_type":"stream","text":["/usr/local/lib/python3.6/dist-packages/keras/utils/data_utils.py:610: UserWarning: The input 3 could not be retrieved. It could be because a worker has died.\n","  UserWarning)\n"],"name":"stderr"},{"output_type":"stream","text":[" 7/22 [========>.....................] - ETA: 8:02 - loss: 0.6115 - acc: 0.6786"],"name":"stdout"},{"output_type":"stream","text":["/usr/local/lib/python3.6/dist-packages/keras/utils/data_utils.py:610: UserWarning: The input 12 could not be retrieved. It could be because a worker has died.\n","  UserWarning)\n"],"name":"stderr"},{"output_type":"stream","text":["10/22 [============>.................] - ETA: 6:24 - loss: 0.6054 - acc: 0.6937"],"name":"stdout"},{"output_type":"stream","text":["/usr/local/lib/python3.6/dist-packages/keras/utils/data_utils.py:610: UserWarning: The input 15 could not be retrieved. It could be because a worker has died.\n","  UserWarning)\n"],"name":"stderr"},{"output_type":"stream","text":["15/22 [===================>..........] - ETA: 3:26 - loss: 0.5944 - acc: 0.7075"],"name":"stdout"},{"output_type":"stream","text":["/usr/local/lib/python3.6/dist-packages/keras/utils/data_utils.py:610: UserWarning: The input 5 could not be retrieved. It could be because a worker has died.\n","  UserWarning)\n"],"name":"stderr"},{"output_type":"stream","text":["19/22 [========================>.....] - ETA: 1:29 - loss: 0.5746 - acc: 0.7197"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"nDysyRVSdLMX","colab_type":"code","colab":{}},"source":[""],"execution_count":0,"outputs":[]}]}