{"cells":[{"metadata":{"trusted":true},"cell_type":"code","source":"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load in \n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the \"../input/\" directory.\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nprint(os.listdir(\"../input\"))\n# Any results you write to the current directory are saved as output.","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"import shutil\nimport csv\nimport tensorflow as tf\nimport keras_preprocessing\nfrom keras_preprocessing import image\nfrom keras_preprocessing.image import ImageDataGenerator","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"df_train=pd.read_csv(r\"../input/train.csv\",delimiter=',')\ndf_test=pd.read_csv(r\"../input/test.csv\",delimiter=',')\nprint(df_train.head())\nprint(df_test.head())\ndf_train['id_code']=df_train['id_code']+'.png'\n#df_train['diagnosis']=df_train.astype({'diagnosis': 'category'})\ndf_train['diagnosis']=df_train['diagnosis'].astype(str)\n#pd.get_dummies(df_train,prefix=['diagnosis'], drop_first=True)\ndf_test['id_code']=df_test['id_code']+'.png'","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_datagen = ImageDataGenerator(\n    rescale = 1./255,\n    rotation_range=30,\n    width_shift_range=0.2,\n    height_shift_range=0.2,\n    shear_range=0.2,\n    zoom_range=0.2,\n    horizontal_flip=True,\n    fill_mode='nearest')\n\nTRAINING_DIR='../input/train_images'\n\ntrain_generator = train_datagen.flow_from_dataframe(\n    dataframe=df_train,\n    directory=TRAINING_DIR,\n    x_col='id_code',\n    y_col='diagnosis',\n    batch_size=20,\n    target_size=(1050,1050),\n    class_mode='sparse'\n)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from tensorflow.keras.optimizers import RMSprop","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model = tf.keras.models.Sequential([\n    tf.keras.layers.Conv2D(16 ,(5,5), activation='relu', input_shape=(1050,1050,3)),\n    tf.keras.layers.MaxPooling2D(2,2),\n    tf.keras.layers.Conv2D(32,(2,2), activation='relu'),\n    tf.keras.layers.MaxPooling2D(2,2),\n    tf.keras.layers.Dropout(0.1),\n    tf.keras.layers.Conv2D(64,(5,5), activation='relu'),\n    tf.keras.layers.MaxPooling2D(3,3),\n    tf.keras.layers.Conv2D(64,(3,3), activation='relu'),\n    tf.keras.layers.MaxPooling2D(2,2),\n    tf.keras.layers.Conv2D(64,(5,5), activation='relu'),\n    tf.keras.layers.MaxPooling2D(2,2),\n    tf.keras.layers.Conv2D(64,(3,3), activation='relu'),\n    tf.keras.layers.Conv2D(64,(2,2), activation='relu'),\n    tf.keras.layers.MaxPooling2D(2,2),\n    tf.keras.layers.Conv2D(64,(5,5), activation='relu'),\n    tf.keras.layers.Dropout(0.05),\n    tf.keras.layers.Flatten(),\n    tf.keras.layers.Dense(1024, activation='relu'),\n    tf.keras.layers.Dropout(0.05),\n    tf.keras.layers.Dense(5, activation='softmax')\n    \n    \n])\n\nmodel.summary()\nmodel.compile(loss='sparse_categorical_crossentropy',optimizer=RMSprop(lr=0.001),metrics=['accuracy'])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"history=model.fit_generator(train_generator,steps_per_epoch=55 ,epochs=50,verbose=1)\nmodel.save(\"first_part.h5\")","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import matplotlib.pyplot as plt\nacc = history.history['acc']\n#val_acc = history.history['val_acc']\nloss = history.history['loss']\n#val_loss = history.history['val_loss']\n\nepochs = range(len(acc))\n\nplt.plot(epochs, acc, 'r', label='Training accuracy')\n#plt.plot(epochs, val_acc, 'b', label='Validation accuracy')\n#plt.title('Training and validation accuracy')\nplt.plot(epochs, loss, 'b', label='Training loss')\nplt.title('Training')\nplt.title('Loss')\nplt.legend(loc=0)\nplt.figure()\n\n\nplt.show()","execution_count":null,"outputs":[]}],"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":1}