{"cells":[{"metadata":{},"cell_type":"markdown","source":"I referred this kernel for leaning how to use Keras.\n\n[Keras Starter](https://www.kaggle.com/ateplyuk/keras-starter)"},{"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 os","execution_count":37,"outputs":[]},{"metadata":{"_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","trusted":true},"cell_type":"code","source":"train = pd.read_csv(\"../input/imet-2019-fgvc6/train.csv\")\nlabels = pd.read_csv(\"../input/imet-2019-fgvc6/labels.csv\")\nsub = pd.read_csv(\"../input/imet-2019-fgvc6/sample_submission.csv\")\n\ntrain[\"id\"] = train.id.map(lambda x: \"{}.png\".format(x))\ntrain[\"attribute_ids\"] = train.attribute_ids.map(lambda x: x.split())\nsub[\"id\"] = sub.id.map(lambda x: \"{}.png\".format(x))\n\ndisplay(sub.head())\ndisplay(train.head())","execution_count":39,"outputs":[{"output_type":"display_data","data":{"text/plain":" id attribute_ids\n0 10023b2cc4ed5f68.png 0 1 2\n1 100fbe75ed8fd887.png 0 1 2\n2 101b627524a04f19.png 0 1 2\n3 10234480c41284c6.png 0 1 2\n4 1023b0e2636dcea8.png 0 1 2","text/html":"
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idattribute_ids
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"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":" id attribute_ids\n0 1000483014d91860.png [147, 616, 813]\n1 1000fe2e667721fe.png [51, 616, 734, 813]\n2 1001614cb89646ee.png [776]\n3 10041eb49b297c08.png [51, 671, 698, 813, 1092]\n4 100501c227f8beea.png [13, 404, 492, 903, 1093]","text/html":"
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idattribute_ids
01000483014d91860.png[147, 616, 813]
11000fe2e667721fe.png[51, 616, 734, 813]
21001614cb89646ee.png[776]
310041eb49b297c08.png[51, 671, 698, 813, 1092]
4100501c227f8beea.png[13, 404, 492, 903, 1093]
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"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"batch_size = 32\nimg_size = 64\nnb_epochs = 150\nnb_classes = labels.shape[0]\nlbls = list(map(str, range(nb_classes)))","execution_count":41,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"%%time\n\nfrom keras.preprocessing.image import ImageDataGenerator\n\ntrain_datagen = ImageDataGenerator(rescale=1./255, validation_split=0.25)\n\ntrain_generator=train_datagen.flow_from_dataframe(\n dataframe=train,\n directory=\"../input/imet-2019-fgvc6/train\",\n x_col=\"id\",\n y_col=\"attribute_ids\",\n batch_size=batch_size,\n shuffle=True,\n class_mode=\"categorical\",\n classes=lbls,\n target_size=(img_size,img_size),\n subset='training')\n\nvalid_generator=train_datagen.flow_from_dataframe(\n dataframe=train,\n directory=\"../input/imet-2019-fgvc6/train\",\n x_col=\"id\",\n y_col=\"attribute_ids\",\n batch_size=batch_size,\n shuffle=True,\n class_mode=\"categorical\", \n classes=lbls,\n target_size=(img_size,img_size),\n subset='validation')\n\n\ntest_datagen = ImageDataGenerator(rescale=1./255)\n\ntest_generator = test_datagen.flow_from_dataframe( \n dataframe=sub,\n directory = \"../input/imet-2019-fgvc6/test\", \n x_col=\"id\",\n target_size = (img_size,img_size),\n batch_size = 1,\n shuffle = False,\n class_mode = None\n )","execution_count":47,"outputs":[{"output_type":"stream","text":"Found 81928 images belonging to 1103 classes.\nFound 27309 images belonging to 1103 classes.\nFound 7443 images.\nCPU times: user 5.77 s, sys: 4.6 s, total: 10.4 s\nWall time: 1min 18s\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"from keras.applications.vgg16 import VGG16\nfrom keras.layers import Dropout\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Flatten\n\nvgg_conv = VGG16(weights=None, include_top=False, input_shape=(img_size, img_size, 3))\nvgg_conv.load_weights('../input/vgg16/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5')\n\nfor layer in vgg_conv.layers[:-4]:\n layer.trainable = False\n\nmodel = Sequential()\nmodel.add(vgg_conv)\n \nmodel.add(Flatten())\nmodel.add(Dense(1024, activation='relu'))\nmodel.add(Dropout(0.5))\nmodel.add(Dense(nb_classes, activation='softmax'))\n \nmodel.summary()","execution_count":48,"outputs":[{"output_type":"stream","text":"_________________________________________________________________\nLayer (type) Output Shape Param # \n=================================================================\nvgg16 (Model) (None, 2, 2, 512) 14714688 \n_________________________________________________________________\nflatten_8 (Flatten) (None, 2048) 0 \n_________________________________________________________________\ndense_15 (Dense) (None, 1024) 2098176 \n_________________________________________________________________\ndropout_8 (Dropout) (None, 1024) 0 \n_________________________________________________________________\ndense_16 (Dense) (None, 1103) 1130575 \n=================================================================\nTotal params: 17,943,439\nTrainable params: 10,308,175\nNon-trainable params: 7,635,264\n_________________________________________________________________\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"from keras import optimizers\n\nmodel.compile(optimizers.rmsprop(lr=0.0001, decay=1e-6),loss=\"categorical_crossentropy\",metrics=[\"accuracy\"])","execution_count":49,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"history = model.fit_generator(\n generator=train_generator,\n steps_per_epoch=100,\n validation_data=valid_generator,\n validation_steps=50,\n epochs=nb_epochs,\n verbose=1)","execution_count":50,"outputs":[{"output_type":"stream","text":"Epoch 1/50\n100/100 [==============================] - 39s 394ms/step - loss: 18.1495 - acc: 0.0609 - val_loss: 16.2219 - val_acc: 0.1212\nEpoch 2/50\n100/100 [==============================] - 37s 366ms/step - loss: 16.5119 - acc: 0.0872 - val_loss: 15.9322 - val_acc: 0.1281\nEpoch 3/50\n100/100 [==============================] - 37s 372ms/step - loss: 16.0891 - acc: 0.1016 - val_loss: 15.4635 - val_acc: 0.0994\nEpoch 4/50\n100/100 [==============================] - 37s 371ms/step - loss: 16.1413 - acc: 0.0994 - val_loss: 15.4923 - val_acc: 0.1087\nEpoch 5/50\n100/100 [==============================] - 35s 352ms/step - loss: 15.6467 - acc: 0.1147 - val_loss: 15.4462 - val_acc: 0.1319\nEpoch 6/50\n100/100 [==============================] - 36s 356ms/step - loss: 15.3330 - acc: 0.1194 - val_loss: 14.8392 - val_acc: 0.1275\nEpoch 7/50\n 96/100 [===========================>..] - ETA: 1s - loss: 15.2040 - acc: 0.1276","name":"stdout"},{"output_type":"error","ename":"KeyboardInterrupt","evalue":"","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)","\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mvalidation_steps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m50\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnb_epochs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m verbose=1)\n\u001b[0m","\u001b[0;32m/opt/conda/lib/python3.6/site-packages/keras/legacy/interfaces.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 89\u001b[0m warnings.warn('Update your `' + object_name + '` call to the ' +\n\u001b[1;32m 90\u001b[0m 'Keras 2 API: ' + signature, stacklevel=2)\n\u001b[0;32m---> 91\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 92\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_original_function\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 93\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/opt/conda/lib/python3.6/site-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit_generator\u001b[0;34m(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)\u001b[0m\n\u001b[1;32m 1416\u001b[0m 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683\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 684\u001b[0m \u001b[0;32mwhile\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_running\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;32m--> 685\u001b[0;31m \u001b[0minputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mqueue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mblock\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 686\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mqueue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtask_done\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 687\u001b[0m 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\u001b[0msignaled\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 551\u001b[0;31m \u001b[0msignaled\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cond\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 552\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0msignaled\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 553\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/opt/conda/lib/python3.6/threading.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m 293\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# restore state no matter what (e.g., KeyboardInterrupt)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 294\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 295\u001b[0;31m \u001b[0mwaiter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0macquire\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 296\u001b[0m \u001b[0mgotit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 297\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mKeyboardInterrupt\u001b[0m: "]}]},{"metadata":{"trusted":true},"cell_type":"code","source":"import json\n\nwith open('history.json', 'w') as f:\n json.dump(history.history, f)\n\nhistory_df = pd.DataFrame(history.history)\nhistory_df[['loss', 'val_loss']].plot()\nhistory_df[['acc', 'val_acc']].plot()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"%%time\n\ntest_generator.reset()\npredict=model.predict_generator(test_generator, steps = len(test_generator.filenames))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"predicted_class_indices = np.argmax(predict,axis=1)\n\nlabels = (train_generator.class_indices)\nlabels = dict((v,k) for k,v in labels.items())\npredictions = [labels[k] for k in predicted_class_indices]\n\nsub[\"attribute_ids\"] = predictions\nsub['id'] = sub['id'].map(lambda x: str(x)[:-4])\n\nsub.to_csv(\"submission.csv\",index=False)\nsub.head()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"","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}