IncResV2.ipynb

{"cells":[{"metadata":{"_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","trusted":true},"cell_type":"code","source":"import numpy as np\nimport os\n\n\n\ndata = np.load(\"../input/practice/224_data.npy\")\nlabels = np.load(\"../input/practice/224_labels.npy\")\n\nprint(data.shape)\nprint(labels.shape)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.model_selection import train_test_split\nX_train, X_test, y_train, y_test = train_test_split(data, labels, test_size=0.2, random_state=42)\nprint(X_train.shape)\nprint(X_test.shape)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from keras.applications.inception_resnet_v2 import InceptionResNetV2\nfrom keras.layers import Dense, Dropout, GlobalAveragePooling2D\nfrom keras.models import Model\nfrom keras.callbacks import ModelCheckpoint\n\n\n#IncRes.summary()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"IncRes= InceptionResNetV2(weights='imagenet',include_top=False,input_shape=(224,224,3))\nIncRes = Model(input=IncRes.input,output=IncRes.get_layer('block17_9_ac').output)\n\nx = IncRes.output\nx = GlobalAveragePooling2D()(x)\nx = Dense(1024,activation='relu')(x)\nx = Dropout(0.5)(x)\nprediction = Dense(2, activation='softmax', name='prediction')(x)\n\nfrom keras.optimizers import SGD\nfrom keras.optimizers import RMSprop\n\nmodel = Model(inputs=IncRes.input,outputs=prediction)\n\nrms = RMSprop(lr=0.000001)\nmodel.compile(optimizer=rms,\n              loss='categorical_crossentropy',\n              metrics=['accuracy'])\n\nhist = model.fit(X_train, y_train,\n      batch_size=64,\n      epochs=30,\n      shuffle=True,\n      validation_split=0.1,\n      verbose=1)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.metrics import accuracy_score\nfrom sklearn.metrics import confusion_matrix\nfrom sklearn.metrics import f1_score\nfrom sklearn.metrics import recall_score\n\n\n#model.load_weights(\"weights.best.hdf5\")\ny_pred = model.predict(X_test)\n\n\nprint(\"Accuracy: \",accuracy_score(y_test.argmax(axis=1),y_pred.argmax(axis=1)))\nprint(\"F1_score: \",f1_score(y_test.argmax(axis=1),y_pred.argmax(axis=1)))\nprint(\"Recall: \",recall_score(y_test.argmax(axis=1),y_pred.argmax(axis=1)))\nprint(\"Confusion Matrix\",confusion_matrix(y_test.argmax(axis=1),y_pred.argmax(axis=1)))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import matplotlib.pyplot as plt\n\n## Plot Accuracy History\n\nplt.plot(hist.history['acc'])\nplt.plot(hist.history['val_acc'])\nplt.title('Model accuracy')\nplt.ylabel('Accuracy')\nplt.xlabel('Epoch')\nplt.legend(['Train', 'Validation'], loc='lower right')\nplt.show()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"## Plot Loss History\n\nplt.plot(hist.history['loss'])\nplt.plot(hist.history['val_loss'])\nplt.title('Model loss')\nplt.ylabel('Loss')\nplt.xlabel('Epoch')\nplt.legend(['Train', 'Validation'], loc='upper right')\nplt.show()","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"name":"python","version":"3.6.4","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat":4,"nbformat_minor":1}