Sai Ajay ajaymuktha

## split.py
X = dataset.iloc[:, :-1].values
y = dataset.iloc[:, -1].values

## data.py
dataset = pd.read_csv('Data.csv')

## lib.py
import numpy as np
import pandas as pd

## fit.py
r = model.fit_generator(
  training_set,
  validation_data=test_set,
  epochs=5,
  steps_per_epoch=len(training_set),
  validation_steps=len(test_set)
)

## test.py
test_set = test_datagen.flow_from_directory('Datasets/test',
                                            target_size = (224, 224),
                                            batch_size = 32,
                                            class_mode = 'categorical')

## train.py
training_set = train_datagen.flow_from_directory('Datasets/train',
                                                 target_size = (224, 224),
                                                 batch_size = 32,
                                                 class_mode = 'categorical')

## datagen.py
from keras.preprocessing.image import ImageDataGenerator

train_datagen = ImageDataGenerator(rescale = 1./255,
                                   shear_range = 0.2,
                                   zoom_range = 0.2,
                                   horizontal_flip = True)

test_datagen = ImageDataGenerator(rescale = 1./255)

## compile.py
model.compile(
  loss='categorical_crossentropy',
  optimizer='adam',
  metrics=['accuracy']
)

## summary.py
model.summary()

## model.py
model = Model(inputs=vgg.input, outputs=prediction)
	X = dataset.iloc[:, :-1].values
	y = dataset.iloc[:, -1].values
	r = model.fit_generator(
	training_set,
	validation_data=test_set,
	epochs=5,
	steps_per_epoch=len(training_set),
	validation_steps=len(test_set)
	)
	test_set = test_datagen.flow_from_directory('Datasets/test',
	target_size = (224, 224),
	batch_size = 32,
	class_mode = 'categorical')
	training_set = train_datagen.flow_from_directory('Datasets/train',
	target_size = (224, 224),
	batch_size = 32,
	class_mode = 'categorical')
	from keras.preprocessing.image import ImageDataGenerator

	train_datagen = ImageDataGenerator(rescale = 1./255,
	shear_range = 0.2,
	zoom_range = 0.2,
	horizontal_flip = True)

	test_datagen = ImageDataGenerator(rescale = 1./255)
	model.compile(
	loss='categorical_crossentropy',
	optimizer='adam',
	metrics=['accuracy']
	)