{"cells":[{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"import numpy as np\nimport pandas as pd\nimport cv2\nfrom tqdm import tqdm\nfrom random import shuffle\nimport os\n\nTrain_dir = \"../input/train/\"\nTest_dir = \"../input/test/\"\n\nimg_size = 50\nLR = 1e-3","execution_count":11,"outputs":[]},{"metadata":{"_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","trusted":true},"cell_type":"code","source":"# label the images\ndef label_img(img):\n    word_label = img.split('.')[-3]\n    if word_label == 'cat':\n        return [1,0]\n    elif word_label == 'dog':\n        return [0,1]","execution_count":12,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# process training images\ndef create_training_data():\n    training_data = []\n    for img in tqdm(os.listdir(Train_dir)):\n        label = label_img(img)\n        path = os.path.join(Train_dir,img)\n        img = cv2.imread(path, cv2.IMREAD_GRAYSCALE)\n        img = cv2.resize(img, (img_size, img_size))\n        training_data.append([np.array(img), np.array(label)])\n    shuffle(training_data)\n    return training_data","execution_count":13,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# process test data, note that this is unlabelled data\ndef process_test_data():\n    testing_data = []\n    for img in tqdm(os.listdir(Test_dir)):\n        path = os.path.join(Test_dir, img)\n        img_num = img.split('.')[0]\n        img = cv2.imread(path, cv2.IMREAD_GRAYSCALE)\n        img = cv2.resize(img, (img_size, img_size))\n        testing_data.append([np.array(img), img_num])    \n    shuffle(testing_data)\n    return testing_data","execution_count":14,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# create array from test images\ntrain_data = create_training_data()\ntest_data = process_test_data()","execution_count":15,"outputs":[{"output_type":"stream","text":"100%|██████████| 25000/25000 [00:46<00:00, 539.40it/s]\n100%|██████████| 12500/12500 [00:22<00:00, 552.94it/s]\n","name":"stderr"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# use tflearn to define our neural network\n\nimport tflearn\nfrom tflearn.layers.conv import conv_2d, max_pool_2d\nfrom tflearn.layers.core import input_data, dropout, fully_connected\nfrom tflearn.layers.estimator import regression","execution_count":18,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"convnet = input_data(shape=[None, img_size, img_size, 1], name='input')\n\nconvnet = conv_2d(convnet, 32, 5, activation='relu')\nconvnet = max_pool_2d(convnet, 5)\n\nconvnet = conv_2d(convnet, 64, 5, activation='relu')\nconvnet = max_pool_2d(convnet, 5)\n\nconvnet = fully_connected(convnet, 1024, activation='relu')\nconvnet = dropout(convnet, 0.8)\n\nconvnet = fully_connected(convnet, 2, activation='softmax')\nconvnet = regression(convnet, optimizer='adam', learning_rate=LR, loss='categorical_crossentropy', name='targets')\n\nmodel = tflearn.DNN(convnet, tensorboard_dir='log')","execution_count":19,"outputs":[{"output_type":"stream","text":"WARNING:tensorflow:From /opt/conda/lib/python3.6/site-packages/tflearn/initializations.py:110: UniformUnitScaling.__init__ (from tensorflow.python.ops.init_ops) is deprecated and will be removed in a future version.\nInstructions for updating:\nUse tf.initializers.variance_scaling instead with distribution=uniform to get equivalent behavior.\nWARNING:tensorflow:From /opt/conda/lib/python3.6/site-packages/tflearn/layers/core.py:247: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\nInstructions for updating:\nPlease use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\nWARNING:tensorflow:From /opt/conda/lib/python3.6/site-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\nInstructions for updating:\nUse tf.cast instead.\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# we look for previously saved model so as to save training time\n\nif os.path.exists('{}.meta'.format(model_name)):\n    model.load(model_name)\n    print('model loaded!')","execution_count":20,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# split our training data into train and test data inorder to check accuracy`\ntrain = train_data[:-500]\ntest = train_data[-500:]\n\n# create data arrays\ntrain_X = np.array([i[0] for i in train]).reshape(-1,img_size,img_size,1)\ntrain_y = [i[1] for i in train]\n\ntest_X = np.array([i[0] for i in test]).reshape(-1,img_size,img_size,1)\ntest_y = [i[1] for i in test]","execution_count":21,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# fit the model with 3 epochs\n\nmodel.fit({'input': train_X}, {'targets': train_y}, n_epoch=3, validation_set=({'input': test_X}, {'targets': test_y}), \n    snapshot_step=500, show_metric=True, run_id=model_name)","execution_count":22,"outputs":[{"output_type":"stream","text":"Training Step: 1148  | total loss: \u001b[1m\u001b[32m11.24643\u001b[0m\u001b[0m | time: 43.691s\n| Adam | epoch: 003 | loss: 11.24643 - acc: 0.5116 -- iter: 24448/24500\nTraining Step: 1149  | total loss: \u001b[1m\u001b[32m11.23710\u001b[0m\u001b[0m | time: 44.805s\n| Adam | epoch: 003 | loss: 11.23710 - acc: 0.5120 | val_loss: 11.19056 - val_acc: 0.5140 -- iter: 24500/24500\n--\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# we need to reset the graph instance, since we're doing this in a continuous environment:\n\nimport tensorflow as tf\ntf.reset_default_graph()","execution_count":23,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"convnet = input_data(shape=[None, img_size, img_size, 1], name='input')\n\nconvnet = conv_2d(convnet, 32, 5, activation='relu')\nconvnet = max_pool_2d(convnet, 5)\n\nconvnet = conv_2d(convnet, 64, 5, activation='relu')\nconvnet = max_pool_2d(convnet, 5)\n\nconvnet = conv_2d(convnet, 128, 5, activation='relu')\nconvnet = max_pool_2d(convnet, 5)\n\nconvnet = conv_2d(convnet, 64, 5, activation='relu')\nconvnet = max_pool_2d(convnet, 5)\n\nconvnet = conv_2d(convnet, 32, 5, activation='relu')\nconvnet = max_pool_2d(convnet, 5)\n\nconvnet = fully_connected(convnet, 1024, activation='relu')\nconvnet = dropout(convnet, 0.8)\n\nconvnet = fully_connected(convnet, 2, activation='softmax')\nconvnet = regression(convnet, optimizer='adam', learning_rate=LR, loss='categorical_crossentropy', name='targets')\n\nmodel = tflearn.DNN(convnet, tensorboard_dir='log')\n\n\n\nif os.path.exists('{}.meta'.format(model_name)):\n    model.load(model_name)\n    print('model loaded!')\n\nmodel.fit({'input': train_X}, {'targets': train_y}, n_epoch=3, validation_set=({'input': test_X}, {'targets': test_y}), \n    snapshot_step=500, show_metric=True, run_id=model_name)","execution_count":24,"outputs":[{"output_type":"stream","text":"Training Step: 1148  | total loss: \u001b[1m\u001b[32m0.45678\u001b[0m\u001b[0m | time: 48.045s\n| Adam | epoch: 003 | loss: 0.45678 - acc: 0.7857 -- iter: 24448/24500\nTraining Step: 1149  | total loss: \u001b[1m\u001b[32m0.44519\u001b[0m\u001b[0m | time: 49.180s\n| Adam | epoch: 003 | loss: 0.44519 - acc: 0.7946 | val_loss: 0.58745 - val_acc: 0.6980 -- iter: 24500/24500\n--\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Visualize the unlabelled test data\n\nimport matplotlib.pyplot as plt\n%matplotlib inline","execution_count":25,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"fig=plt.figure(figsize=(16,8))\n\nfor num,data in enumerate(test_data[:12]): #plot first 12 images\n    # cat: [1,0]\n    # dog: [0,1]\n    \n    img_num = data[1]\n    img_data = data[0]\n    \n    y = fig.add_subplot(3,4,num+1)\n    orig = img_data\n    data = img_data.reshape(img_size,img_size,1)\n    model_out = model.predict([data])[0]\n    \n    if np.argmax(model_out) == 1: str_label='Dog'\n    else: str_label='Cat'\n        \n    y.imshow(orig,cmap='gray')\n    plt.title(str_label)\n    y.axes.get_xaxis().set_visible(False)\n    y.axes.get_yaxis().set_visible(False)\nplt.show()","execution_count":26,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1152x576 with 12 Axes>","image/png":"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\n"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"display_name":"Python 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