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#!/usr/bin/env python
# coding: utf-8
# In[1]:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
# In[2]:
import tensorflow as tf
from tensorflow import keras
from keras import Sequential
from keras.layers import Dense,Convolution2D,Flatten,Dropout,BatchNormalization
from tensorflow.keras.layers import MaxPooling2D
from keras.preprocessing.image import ImageDataGenerator
# In[ ]:
#val_data=keras.utils.image_dataset_from_directory(
#directory="E:\DSspec\Internship\CUB-200-2011\cub_200_2011_64x64_for_fid_10k\cub_200_2011_64x64_10k"
#label="inferred",
#label_mode="int",
#batch_size=32,
#iamge_size=(256,256)
#)
# In[3]:
train=keras.utils.image_dataset_from_directory(directory="E:\\DSspec\\Internship\\CUB-200-2011\\cub_200_2011_64x64_for_fid_10k",
labels="inferred",
validation_split=0.2,
subset="training",
seed=1337,
label_mode="int",
batch_size=32,
image_size=(256,256))
# In[4]:
test=keras.utils.image_dataset_from_directory(directory="E:\\DSspec\\Internship\\CUB-200-2011\\cub_200_2011_64x64_for_fid_10k",
labels="inferred",
validation_split=0.2,
subset="validation",
seed=1337,
label_mode="int",
batch_size=32,
image_size=(256,256))
# In[5]:
for image,label in train.take(2):
plt.imshow(image[31].numpy().astype("uint8"))
plt.show()
# In[8]:
from tensorflow.keras import layers
data_augmentation = keras.Sequential(
[
layers.RandomFlip("horizontal", input_shape=(256, 256, 3)),
layers.RandomRotation(0.3),
layers.RandomZoom(0.3),
]
)
# In[9]:
train_gen = train.map(lambda x, y: (data_augmentation(x, training=True), y))
# In[ ]:
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