Upload Data_preprocessing.py

Data_preprocessing.py

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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[ ]:
+
+
+
+