{"cells":[{"metadata":{},"cell_type":"markdown","source":"# Acknowledgements\n\n- Thanks to Xhulu for their wonderful kernal.[click here](https://www.kaggle.com/xhlulu/alaska2-efficientnet-on-tpus)\n- Trying different model like resnet101, Xception model, inception_v2, vgg16, Densenet121.\n- Experimented with number of epochs.","execution_count":null},{"metadata":{},"cell_type":"markdown","source":"# Other References And some wonderful kernal for kick start...\n\n- Best and well documented kernal for kick start is mr.Tanul singh.[Click here](https://www.kaggle.com/tanulsingh077/steganalysis-complete-understanding-and-model)\n- Another kernal is of Mr.Prasant banarjee.[click here](https://www.kaggle.com/prashant111/alaska2-image-steganalysis-all-you-need-to-know)\n- Another best kernal is of Wei hao khoong.[click here](https://www.kaggle.com/khoongweihao/alaska2-blending-efficientnets-on-tpus)\n- Kernal of mine on handwritten grapheme.[click here](https://www.kaggle.com/saife245/handwritten-grapheme-classification-resnet-0-97)","execution_count":null},{"metadata":{},"cell_type":"markdown","source":"## My Other Works on kaggle...\n\n- NeuroImaging problem on kaggle.[click here](https://www.kaggle.com/saife245/neuroimaging-in-depth-understanding-eda-model)\n- Football match prediction.[click here](https://www.kaggle.com/saife245/football-match-prediction)\n- Hand written Grapheme Classification.[click here](https://www.kaggle.com/saife245/handwritten-grapheme-classification-resnet-0-97)\n- Cutmix_Gridmask_Mixup_Cutout.[click here](https://www.kaggle.com/saife245/cutmix-vs-mixup-vs-gridmask-vs-cutout)\n","execution_count":null},{"metadata":{},"cell_type":"markdown","source":"# Imports","execution_count":null},{"metadata":{"_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","trusted":true},"cell_type":"code","source":"import math, re, os\n\nimport numpy as np\nimport pandas as pd\nfrom matplotlib import pyplot as plt\nfrom kaggle_datasets import KaggleDatasets\nimport tensorflow as tf\nimport tensorflow.keras.layers as L\nimport tensorflow.keras.applications.xception as xcep\nimport tensorflow.keras.applications.densenet as dense\nfrom sklearn import metrics\nfrom sklearn.model_selection import train_test_split","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# TPU Strategy and other configs ","execution_count":null},{"metadata":{"trusted":true},"cell_type":"code","source":"# Detect hardware, return appropriate distribution strategy\ntry:\n    # TPU detection. No parameters necessary if TPU_NAME environment variable is\n    # set: this is always the case on Kaggle.\n    tpu = tf.distribute.cluster_resolver.TPUClusterResolver()\n    print('Running on TPU ', tpu.master())\nexcept ValueError:\n    tpu = None\n\nif tpu:\n    tf.config.experimental_connect_to_cluster(tpu)\n    tf.tpu.experimental.initialize_tpu_system(tpu)\n    strategy = tf.distribute.experimental.TPUStrategy(tpu)\nelse:\n    # Default distribution strategy in Tensorflow. Works on CPU and single GPU.\n    strategy = tf.distribute.get_strategy()\n\nprint(\"REPLICAS: \", strategy.num_replicas_in_sync)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# For tf.dataset\nAUTO = tf.data.experimental.AUTOTUNE\n\n# Data access\nGCS_DS_PATH = KaggleDatasets().get_gcs_path()\n\n# Configuration\nEPOCHS = 10 # original 10\nBATCH_SIZE = 16 * strategy.num_replicas_in_sync","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Load label and paths","execution_count":null},{"metadata":{"trusted":true},"cell_type":"code","source":"def append_path(pre):\n    return np.vectorize(lambda file: os.path.join(GCS_DS_PATH, pre, file))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"sub = pd.read_csv('/kaggle/input/alaska2-image-steganalysis/sample_submission.csv')\ntrain_filenames = np.array(os.listdir(\"/kaggle/input/alaska2-image-steganalysis/Cover/\"))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"np.random.seed(0)\npositives = train_filenames.copy()\nnegatives = train_filenames.copy()\nnp.random.shuffle(positives)\nnp.random.shuffle(negatives)\n\njmipod = append_path('JMiPOD')(positives[:10000])\njuniward = append_path('JUNIWARD')(positives[10000:20000])\nuerd = append_path('UERD')(positives[20000:30000])\n\npos_paths = np.concatenate([jmipod, juniward, uerd])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"test_paths = append_path('Test')(sub.Id.values)\nneg_paths = append_path('Cover')(negatives[:30000])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_paths = np.concatenate([pos_paths, neg_paths])\ntrain_labels = np.array([1] * len(pos_paths) + [0] * len(neg_paths))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_paths, valid_paths, train_labels, valid_labels = train_test_split(\n    train_paths, train_labels, test_size=0.15, random_state=2020)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Create Dataset objects","execution_count":null},{"metadata":{"_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","trusted":true},"cell_type":"code","source":"def decode_image(filename, label=None, image_size=(512, 512)):\n    bits = tf.io.read_file(filename)\n    image = tf.image.decode_jpeg(bits, channels=3)\n    image = tf.cast(image, tf.float32) / 255.0\n    image = tf.image.resize(image, image_size)\n    \n    if label is None:\n        return image\n    else:\n        return image, label\n\ndef data_augment(image, label=None):\n    image = tf.image.random_flip_left_right(image)\n    image = tf.image.random_flip_up_down(image)\n    #adding extra augmentation\n    image = tf.image.random_saturation(image, 0, 2)\n    image = tf.image.random_brightness(x, 0.2)\n    \n    if label is None:\n        return image\n    else:\n        return image, label","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_dataset = (\n    tf.data.Dataset\n    .from_tensor_slices((train_paths, train_labels))\n    .map(decode_image, num_parallel_calls=AUTO)\n    .cache()\n    .repeat()\n    .shuffle(1024)\n    .batch(BATCH_SIZE)\n    .prefetch(AUTO)\n)\n\nvalid_dataset = (\n    tf.data.Dataset\n    .from_tensor_slices((valid_paths, valid_labels))\n    .map(decode_image, num_parallel_calls=AUTO)\n    .batch(BATCH_SIZE)\n    .cache()\n    .prefetch(AUTO)\n)\n\ntest_dataset = (\n    tf.data.Dataset\n    .from_tensor_slices(test_paths)\n    .map(decode_image, num_parallel_calls=AUTO)\n    .batch(BATCH_SIZE)\n)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## Let analyze the few model output....\n### NOte;- model performance after 10 Epochs\n- Model - Resnet101\n- loss: 0.6454 \n- accuracy: 0.5869 \n- val_loss: 0.6673 \n- val_accuracy: 0.5621\n![resnet101.JPG](attachment:resnet101.JPG)\n\n- Model - InceptionResNetV2\n- loss: 0.6852 \n- accuracy: 0.5263 \n- val_loss: 0.6972 \n- val_accuracy: 0.4853\n![inseption_v2.JPG](attachment:inseption_v2.JPG)\n\n- Model - Squeezenet\n![Squeeze_model.JPG](attachment:Squeeze_model.JPG)\n- loss: 4.1260  \n- accuracy: 0.2927 \n- val_loss: 4.0705 \n- val_accuracy: 0.4789\n![Squeeze%20net.JPG](attachment:Squeeze%20net.JPG)\n\n- MOdel -Xception\n- loss: 0.5662 \n- accuracy: 0.6784 \n- val_loss: 0.6729 \n- val_accuracy: 0.5794\n\n\n- Model - Densenet\n- loss: 0.5908 \n- accuracy: 0.6535 \n- val_loss: 0.6493 \n- val_accuracy: 0.5828\n\n### Best Accuracy obtain at Densenet101 and 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Modelling","execution_count":null},{"metadata":{},"cell_type":"markdown","source":"## Helper Functions","execution_count":null},{"metadata":{"trusted":true},"cell_type":"code","source":"def build_lrfn(lr_start=0.00001, lr_max=0.00008, \n               lr_min=0.000001, lr_rampup_epochs=5, \n               lr_sustain_epochs=0, lr_exp_decay=.8):\n    lr_max = lr_max * strategy.num_replicas_in_sync\n\n    def lrfn(epoch):\n        if epoch < lr_rampup_epochs:\n            lr = (lr_max - lr_start) / lr_rampup_epochs * epoch + lr_start\n        elif epoch < lr_rampup_epochs + lr_sustain_epochs:\n            lr = lr_max\n        else:\n            lr = (lr_max - lr_min) * lr_exp_decay**(epoch - lr_rampup_epochs - lr_sustain_epochs) + lr_min\n        return lr\n    \n    return lrfn","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## Load Model into TPU","execution_count":null},{"metadata":{"trusted":true},"cell_type":"code","source":"with strategy.scope():\n    # Xception model\n    model = tf.keras.Sequential([\n        xcep.Xception(\n            input_shape=(512, 512, 3),\n            weights='imagenet',\n            include_top=False\n        ),\n        L.GlobalAveragePooling2D(),\n        L.Dense(1, activation='sigmoid')\n    ])\n        \n    model.compile(\n        optimizer='adam',\n        loss = 'binary_crossentropy',\n        metrics=['accuracy']\n    )\n    \n    # Densenet121 model\n    model2 = tf.keras.Sequential([\n        dense.DenseNet121(\n            input_shape=(512, 512, 3),\n            weights='imagenet',\n            include_top=False\n        ),\n        L.GlobalAveragePooling2D(),\n        L.Dense(1, activation='sigmoid')\n    ])\n        \n    model2.compile(\n        optimizer='adam',\n        loss = 'binary_crossentropy',\n        metrics=['accuracy']\n    )\n    ","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"reduce_lr_loss = tf.keras.callbacks.ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=5, verbose=1, epsilon=1e-4, mode='min')","execution_count":null,"outputs":[]},{"metadata":{"_kg_hide-output":true,"trusted":true},"cell_type":"code","source":"lrfn = build_lrfn()\nlr_schedule = tf.keras.callbacks.LearningRateScheduler(lrfn, verbose=1)\nSTEPS_PER_EPOCH = train_labels.shape[0] // BATCH_SIZE","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## Training Xception model","execution_count":null},{"metadata":{"trusted":true},"cell_type":"code","source":"model.summary()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"collapsed":true},"cell_type":"code","source":"history = model.fit(\n    train_dataset, \n    epochs=EPOCHS, \n    callbacks=[reduce_lr_loss, lr_schedule],#callbacks=[checkpoint1, reduce_lr_loss, lr_schedule],\n    steps_per_epoch=STEPS_PER_EPOCH,\n    validation_data=valid_dataset\n)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model.save(\"model_Xception.h5\")","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"pred1 = model.predict(test_dataset, verbose=1)\nsub.Label = pred1\nsub.to_csv('submission_xcept.csv', index=False)\nsub.head()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## Training Densenet Model","execution_count":null},{"metadata":{"trusted":true},"cell_type":"code","source":"model2.summary()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"collapsed":true},"cell_type":"code","source":"history2 = model2.fit(\n    train_dataset, \n    epochs=EPOCHS, \n    callbacks=[reduce_lr_loss, lr_schedule],#callbacks=[checkpoint2, reduce_lr_loss, lr_schedule],\n    steps_per_epoch=STEPS_PER_EPOCH,\n    validation_data=valid_dataset\n)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model2.save(\"model_Densenet121.h5\")","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"pred2 = model2.predict(test_dataset, verbose=1) # EfficientNetB7\nsub.Label = pred2\nsub.to_csv('submission_dense.csv', index=False)\nsub.head()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Evaluation","execution_count":null},{"metadata":{"_kg_hide-input":true,"trusted":true},"cell_type":"code","source":"def display_training_curves(training, validation, title, subplot):\n    \"\"\"\n    Source: https://www.kaggle.com/mgornergoogle/getting-started-with-100-flowers-on-tpu\n    \"\"\"\n    if subplot%10==1: # set up the subplots on the first call\n        plt.subplots(figsize=(10,10), facecolor='#F0F0F0')\n        plt.tight_layout()\n    ax = plt.subplot(subplot)\n    ax.set_facecolor('#F8F8F8')\n    ax.plot(training)\n    ax.plot(validation)\n    ax.set_title('model '+ title)\n    ax.set_ylabel(title)\n    #ax.set_ylim(0.28,1.05)\n    ax.set_xlabel('epoch')\n    ax.legend(['train', 'valid.'])","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## Xception Training Curve","execution_count":null},{"metadata":{"_kg_hide-input":true,"trusted":true},"cell_type":"code","source":"display_training_curves(\n    history.history['loss'], \n    history.history['val_loss'], \n    'loss', 211)\ndisplay_training_curves(\n    history.history['accuracy'], \n    history.history['val_accuracy'], \n    'accuracy', 212)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## Densenet121 Training Curve","execution_count":null},{"metadata":{"trusted":true},"cell_type":"code","source":"display_training_curves(\n    history2.history['loss'], \n    history2.history['val_loss'], \n    'loss', 211)\ndisplay_training_curves(\n    history2.history['accuracy'], \n    history2.history['val_accuracy'], \n    'accuracy', 212)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Submission\n\n## Building Ensemble...","execution_count":null},{"metadata":{"trusted":true},"cell_type":"code","source":"sub.Label = 0.51*pred1 + 0.5*pred2\nsub.to_csv('submission.csv', index=False)\nsub.head()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## IF you like the kernal, Please upvote it.","execution_count":null}],"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat":4,"nbformat_minor":4}