shyamal18122000

train_df = pd.read_csv('PM_test.txt', sep=" ", header=None)
train_df.drop(train_df.columns[[26, 27]], axis=1, inplace=True)
train_df.columns = ['id', 'cycle', 'setting1', 'setting2', 'setting3', 's1', 's2', 's3',
                     's4', 's5', 's6', 's7', 's8', 's9', 's10', 's11', 's12', 's13', 's14',
                     's15', 's16', 's17', 's18', 's19', 's20', 's21']

train_df = train_df.sort_values(['id','cycle'])

# read test data - It is the aircraft engine operating data without failure events recorded.
test_df = pd.read_csv('PM_test.txt', sep=" ", header=None)

shyamal18122000

import pandas as pd
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D, MaxPooling2D
from tensorflow.keras.layers import Activation, Flatten, Dropout, Dense
from tensorflow.keras.layers import BatchNormalization
import seaborn as sns
%matplotlib inline
from matplotlib import pyplot as plt

shyamal18122000

train_fashion_data = pd.read_csv("fashion-mnist_train.csv")
test_fashion_data = pd.read_csv("fashion-mnist_test.csv")

shyamal18122000

def preprocessing(raw):
    label = tf.keras.utils.to_categorical(raw.label, 10)
    num_of_images = raw.shape[0]
    x_as_array = raw.values[:,1:]
    x_shaped_array = x_as_array.reshape(num_of_images, 28, 28, 1)
    image = x_shaped_array / 255
    return image, label

X, Y = preprocessing(train_fashion_data)
X_test, Y_test = preprocessing(test_fashion_data)

shyamal18122000

model = Sequential([
    #first convolution
    Conv2D(32, (3,3), padding='same', activation='relu',kernel_initializer='he_normal', input_shape=(28,28, 1)),
    MaxPooling2D(2,2),
    
    #second convolution
    Conv2D(64, (3,3),padding='same', activation='relu'),
    MaxPooling2D(2,2),
    Dropout(0.2),
    

shyamal18122000

train_model = model.fit(
                  X_train, Y_train,
                  batch_size=128,
                  epochs=100,
                  verbose=1,
                  validation_data=(X_val, Y_val)
              )

shyamal18122000

from tensorflow.keras.optimizers import Adam

model.compile(
    loss="categorical_crossentropy",
    optimizer=Adam(lr=0.001),
    metrics=['accuracy']
)

shyamal18122000

import numpy as np
import pandas as pd
import os
import matplotlib.pyplot as plt
import seaborn as sns
from pathlib import Path
from sklearn.preprocessing import LabelEncoder
import PIL
import cv2
import shutil

shyamal18122000

os.mkdir('./images/')
alpha = 'a'
for i in range(0, 26): 
    os.mkdir('./images/' + alpha)
    alpha = chr(ord(alpha) + 1)

for file in os.listdir(rootdir):
    letter = file[0]
    try:
        shutil.copy(rootdir+file, './images/' + letter + '/' + file)

shyamal18122000

model_ckpt = ModelCheckpoint('BrailleNet.h5',save_best_only=True)
reduce_lr = ReduceLROnPlateau(patience=8,verbose=0)
early_stop = EarlyStopping(patience=15,verbose=1)

entry = layers.Input(shape=(28,28,3))
x = layers.SeparableConv2D(64,(3,3),activation='relu')(entry)
x = layers.MaxPooling2D((2,2))(x)
x = layers.SeparableConv2D(128,(3,3),activation='relu')(x)
x = layers.MaxPooling2D((2,2))(x)
x = layers.SeparableConv2D(256,(2,2),activation='relu')(x)