# %% [code] # This Python 3 environment comes with many helpful analytics libraries installed # It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python # For example, here's several helpful packages to load import numpy as np # linear algebra import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv) # Input data files are available in the read-only "../input/" directory # For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory import os for dirname, _, filenames in os.walk('/kaggle/input'): for filename in filenames: print(os.path.join(dirname, filename)) # You can write up to 5GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using "Save & Run All" # You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session # %% [code] # This Python 3 environment comes with many helpful analytics libraries installed # It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python # For example, here's several helpful packages to load in import numpy as np # linear algebra import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv) # Input data files are available in the "../input/" directory. # For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory import os for dirname, _, filenames in os.walk('/kaggle/input'): for filename in filenames: print(os.path.join(dirname, filename)) # Any results you write to the current directory are saved as output. import csv import numpy as np import keras import tensorflow as tf from keras.models import Sequential from keras.layers import Dense,Dropout,Activation,BatchNormalization from tensorflow.keras.preprocessing.image import ImageDataGenerator from tensorflow.keras.callbacks import ReduceLROnPlateau, EarlyStopping from keras import layers,regularizers import pandas as pd from keras.optimizers import Adam,RMSprop decay=1e-4 xtrain = [] ytrain = [] xtest = [] #rough = [] xval = [] yval = [] # XTRAIN. YTRAIN XVAL YVAL XTEST. DATA Extraction import os for dirname, _, filenames in os.walk('/kaggle/input'): for filename in filenames: print(os.path.join(dirname, filename)) train =pd.read_csv(os.path.join(dirname,'train.csv')) # print("train",np.shape(train)) test =pd.read_csv(os.path.join(dirname,'test.csv')) # print("test",np.shape(test)) sample_submission =pd.read_csv(os.path.join(dirname,'sample_submission.csv')) Dig_MNIST=pd.read_csv(os.path.join(dirname,'Dig-MNIST.csv')) xtrain=train.drop('label',axis=1) ytrain=train.label xtest = test.drop('id', axis = 1) xval=Dig_MNIST.drop('label',axis=1) yval=Dig_MNIST.label xtrain=xtrain.values xtest=xtest.values xval=xval.values ytrain=ytrain.values yval=yval.values # print(np.shape(xval)) # print(np.shape(xtrain)) # xtrain=np.append(xtrain,xval,axis=0) # error yahan hai # ytrain=np.append(ytrain,yval,axis=0) # print(np.shape(xtrain)) # xval=xval[7000:,:] # yval=yval[7000:,:] xtrain=xtrain.reshape(-1,28,28,1).astype('float32') xtest=xtest.reshape(-1,28,28,1).astype('float32') xval=xval.reshape(-1,28,28,1).astype('float32') # print(np.shape(xtest)) xtrain=(xtrain-np.mean(xtrain))/255 xtest=(xtest-np.mean(xtest))/255 xval=(xval-np.mean(xval))/255 model=Sequential() # model.add(layers.Conv2D(filters=32,kernel_size=(3, 3), activation='relu', input_shape=(28,28,1))) # model.add(layers.BatchNormalization()) # model.add(layers.Conv2D(filters=32, kernel_size=(3, 3), activation='relu')) # model.add(layers.BatchNormalization()) # model.add(layers.Conv2D(filters=32, kernel_size=(5, 5), activation='relu')) # model.add(layers.BatchNormalization()) # model.add(layers.Dropout(0.4)) # # model.add(layers.MaxPooling2D(pool_size=[2,2], strides=2)) # model.add(layers.Conv2D(filters=64, kernel_size=(5, 5), activation='relu')) # model.add(layers.BatchNormalization()) # # model.add(layers.Dropout(0.1)) # model.add(layers.Conv2D(filters=64, kernel_size=(7, 7),kernel_regularizer=regularizers.l2(decay), activation='relu')) # model.add(layers.BatchNormalization()) # model.add(layers.Conv2D(filters=64, kernel_size=(7, 7),kernel_regularizer=regularizers.l2(decay), activation='relu')) # model.add(layers.BatchNormalization()) # model.add(layers.Dropout(0.4)) # model.add(layers.Conv2D(filters=128, kernel_size=(4, 4),kernel_regularizer=regularizers.l2(decay), activation='relu')) # # model.add(keras.layers.AveragePooling2D(pool_size=[2,2], strides=2)) # model.add(layers.BatchNormalization()) # model.add(keras.layers.Flatten()) # model.add(keras.layers.Dropout(0.4)) # # model.add(Dense(300,activation='relu')) # # model.add(Dense(64,activation='relu')) # model.add(Dense(10,activation='softmax')) ######################################################################################################## model.add(layers.Conv2D(64, (3,3), padding='same', input_shape=(28, 28, 1))) model.add(layers.BatchNormalization(momentum=0.9, epsilon=1e-5, gamma_initializer="uniform")) model.add(layers.LeakyReLU(alpha=0.1)) model.add(layers.Conv2D(64, (3,3), padding='same')) model.add(layers.BatchNormalization(momentum=0.9, epsilon=1e-5, gamma_initializer="uniform")) model.add(layers.LeakyReLU(alpha=0.1)) model.add(layers.Conv2D(64, (3,3), padding='same')) model.add(layers.BatchNormalization(momentum=0.9, epsilon=1e-5, gamma_initializer="uniform")) model.add(layers.LeakyReLU(alpha=0.1)) model.add(layers.MaxPooling2D(2, 2)) model.add(layers.Dropout(0.2)) model.add(layers.Conv2D(128, (3,3), padding='same')) model.add(layers.BatchNormalization(momentum=0.9, epsilon=1e-5, gamma_initializer="uniform")) model.add(layers.LeakyReLU(alpha=0.1)) model.add(layers.Conv2D(128, (3,3), padding='same')) model.add(layers.BatchNormalization(momentum=0.9, epsilon=1e-5, gamma_initializer="uniform")) model.add(layers.LeakyReLU(alpha=0.1)) model.add(layers.Conv2D(128, (3,3), padding='same')) model.add(layers.BatchNormalization(momentum=0.9, epsilon=1e-5, gamma_initializer="uniform")) model.add(layers.LeakyReLU(alpha=0.1)) model.add(layers.MaxPooling2D(2,2)) model.add(layers.Dropout(0.2)) model.add(layers.Conv2D(256, (3,3), padding='same')) model.add(layers.BatchNormalization(momentum=0.9, epsilon=1e-5, gamma_initializer="uniform")) model.add(layers.LeakyReLU(alpha=0.1)) model.add(layers.Conv2D(256, (3,3), padding='same')) model.add(layers.BatchNormalization(momentum=0.9, epsilon=1e-5, gamma_initializer="uniform")) model.add(layers.LeakyReLU(alpha=0.1)) model.add(layers.MaxPooling2D(2,2)) model.add(layers.Dropout(0.2)) model.add(layers.Flatten()) model.add(layers.Dense(256)) model.add(layers.LeakyReLU(alpha=0.1)) model.add(layers.BatchNormalization()) model.add(layers.Dense(256)) model.add(layers.LeakyReLU(alpha=0.1)) model.add(layers.BatchNormalization()) model.add(layers.Dense(10, activation='softmax')) adam_opt = Adam(lr=0.0002, beta_1=0.9, beta_2=0.999, decay=0.0, amsgrad=False) optimizer = RMSprop(learning_rate=0.002,rho=0.9)#,momentum=0.1,epsilon=1e-07,centered=True,name='RMSprop') model.compile(optimizer=optimizer,loss='sparse_categorical_crossentropy',metrics=['accuracy']) datagen = ImageDataGenerator( rotation_range=12, zoom_range=0.35, width_shift_range=0.3, height_shift_range=0.3, # rescale=1./255 ) datagen.fit(xtrain) learning_rate_reduction = tf.keras.callbacks.ReduceLROnPlateau( monitor='loss', # Quantity to be monitored. factor=0.2, # Factor by which the learning rate will be reduced. new_lr = lr * factor patience=2, # The number of epochs with no improvement after which learning rate will be reduced. verbose=1, # 0: quiet - 1: update messages. mode="min", # {auto, min, max}. In min mode, lr will be reduced when the quantity monitored has stopped decreasing; # in the max mode it will be reduced when the quantity monitored has stopped increasing; # in auto mode, the direction is automatically inferred from the name of the monitored quantity. min_delta=0.0001, # threshold for measuring the new optimum, to only focus on significant changes. cooldown=0, # number of epochs to wait before resuming normal operation after learning rate (lr) has been reduced. min_lr=0.00001 # lower bound on the learning rate. ) es = EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=300, restore_best_weights=True) model.fit_generator(datagen.flow(xtrain, ytrain, batch_size=512), steps_per_epoch=len(xtrain)//512, epochs=42, validation_data=(np.array(xval),np.array(yval)), validation_steps=50, callbacks=[learning_rate_reduction, es]) model.summary() # model.fit(np.array(xtrain),np.array(ytrain),epochs=20,batch_size=16,validation_data = (np.array(xval),np.array(yval)), verbose = 2) score=model.evaluate(np.array(xval),np.array(yval),batch_size=512) # ytest=model.predict(xtest) # ytest=np.argmax(ytest,axis=1) # id_col=np.arange(ytest.shape[0]) # # print(id_col) # sample=pd.read_csv(os.path.join(dirname,'sample_submission.csv')) # sample['label']=ytest # sample.to_csv('submission.csv',index=False) ################# # USE IT TODAY ytest = model.predict_classes(xtest) id_col = np.arange(ytest.shape[0]) submission = pd.DataFrame({'id': id_col, 'label': ytest}) submission.to_csv('submission.csv', index = False) ################## # sub_preds = model.predict_classes(test_df) # id_col = np.arange(sub_preds.shape[0]) # submission = pd.DataFrame({'id': id_col, 'label': ytest}) # submission.to_csv('submission.csv', index = False) print("accuracy",score[1])