swanandM

import os
import tensorflow as tf
from keras.utils import plot_model
import pydot
import graphviz
import numpy as np # linear algebra
from sklearn.model_selection import train_test_split
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
from tqdm import tqdm
from numpy import expand_dims, zeros, ones, vstack

swanandM

d_model.summary()
g_model.summary()

with tf.device('/device:GPU:0'):
    train(g_model, d_model, gan_model, dataset, latent_dim)

swanandM

# train the generator and discriminator
def train(g_model, d_model, gan_model, dataset, latent_dim, n_epochs=320, n_batch=64):
    bat_per_epo = int(len(dataset) / n_batch)
    #print('Batches per Epoch is %d' %bat_per_epo)
    half_batch = int(n_batch / 2)
    #print("Half Batch %d" % half_batch)
    # manually enumerate epochs
    for i in range(n_epochs):
        # enumerate batches over the training set
        for j in range(bat_per_epo):

swanandM

# create and save a plot of generated images
def save_plot(examples, epoch, n=7):
	# scale from [-1,1] to [0,1]
	examples = (examples + 1) / 2.0
	# plot images
	for i in range(n * n):
		# define subplot
		pyplot.subplot(n, n, 1 + i)
		# turn off axis
		pyplot.axis('off')

swanandM

# read images from df_4 dataframe into x_train
x_train = []
def load_real_samples():
    for f, breed in tqdm(df_4.values):
        try:
            img = image.load_img(('/storage/train/{}'.format(f)), target_size=(128, 128))
            # convert to float32
            arr1 = image.img_to_array(img, dtype = 'float32')
            # scale images to [-1,1] from [0,255]
            arr = (arr1 - 127.5) / 127.5

swanandM

# define the standalone discriminator model using GAN training hacks 
def define_discriminator(in_shape=(128,128,3)):
    model = Sequential()
    # input layer with image size of 128x128, since its a colored image it has 3 channels
    model.add(Conv2D(16, (3,3), padding='same', input_shape=in_shape))
    model.add(LeakyReLU(alpha=0.2))
    # downsample to 64x64 using strides of 2,2 and use of LeakyReLU
    model.add(Conv2D(8, (3,3), strides=(2,2), padding='same'))
    model.add(LeakyReLU(alpha=0.2))
    # downsample to 32x32

swanandM

#Read all the labels from the CSV file
df_csv = pd.read_csv('/storage/trainLabels.csv')
df_csv['image'] = df_csv['image'].astype(str) + '.jpeg'

## Delete all the images of size zero (0 KB), No need to do this step while rerunning the program ##
## There are multiple reasons for size zero data such as issue while downloading the database, ##
## Limited space on the VM or currupted data from the source. ##
cd /storage/train/
!find /storage/train/ -size 0 -print
!find /storage/train/ -size 0 -delete

swanandM

#save data before one hot encoding
train.to_csv(r'train.csv', header=None, index=None, sep=' ', mode='a')

#Use full training data
LoadData = ImageDataGenerator(
    horizontal_flip=True)

hard_data_gen = LoadData.flow_from_dataframe(
            dataframe= df_train, directory="/storage/train/", 
            x_col="image", y_col= None, shuffle=False,

swanandM

import matplotlib.pyplot as plt
def plot_history(history):
    loss_list = [s for s in history.history.keys() if 'loss' in s and 'val' not in s]
    val_loss_list = [s for s in history.history.keys() if 'loss' in s and 'val' in s]
    acc_list = [s for s in history.history.keys() if 'acc' in s and 'val' not in s]
    val_acc_list = [s for s in history.history.keys() if 'acc' in s and 'val' in s]
    
    if len(loss_list) == 0:
        print('Loss is missing in history')
        return 

swanandM

with tf.device('/device:GPU:0'): 
    m1= model.fit_generator(
            train_generator,
            epochs=5,
            validation_data=valid_generator,
            class_weight = class_weights,
            callbacks= Callbacks,
            verbose=1)