Spaces:
Running
Running
File size: 2,436 Bytes
527d8f9 b9ee29a 2cc8d92 b9ee29a aee66d8 b9ee29a aee66d8 b9ee29a b3797b8 2e1dcb0 7b908dc 977cc9d 26b08fd 977cc9d 5974209 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 |
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D, Flatten, MaxPooling2D, Dense, Dropout, SpatialDropout2D
from tensorflow.keras.losses import sparse_categorical_crossentropy, binary_crossentropy
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import numpy as np
from PIL import Image
def gen_labels():
train = 'Dataset/Train'
train_generator = ImageDataGenerator(rescale=1/255)
train_generator = train_generator.flow_from_directory(train,
target_size=(256, 256),
batch_size=32,
class_mode='sparse')
labels = train_generator.class_indices
labels = dict((v, k) for k, v in labels.items())
return labels
data_augmentation = tf.keras.Sequential([
tf.keras.layers.experimental.preprocessing.Rescaling(1./127.5, offset= -1),
tf.keras.layers.experimental.preprocessing.RandomFlip("horizontal_and_vertical"),
tf.keras.layers.experimental.preprocessing.RandomRotation(0.2),
tf.keras.layers.experimental.preprocessing.RandomZoom(0.2)
], name='data_augmentation')
#Instantiating the base model
input_shape = (256,256,3)
base_model = tf.keras.applications.ResNet50V2(include_top=False, input_shape=input_shape)
#Making the layers of the model trainable
base_model.trainable = True
def preprocess(img_path_or_img):
if isinstance(img_path_or_img, str): # Check if input is a path
img = Image.open(img_path_or_img)
elif isinstance(img_path_or_img, Image.Image): # Check if input is already an Image object
img = img_path_or_img
else:
raise ValueError("Input must be a string path or a PIL Image object.")
img = img.resize((256, 256))
img_array = np.array(img)
img_array = np.expand_dims(img_array, axis=0)
return img_array
def model_arc():
model = tf.keras.Sequential([
data_augmentation,
base_model,
tf.keras.layers.GlobalAveragePooling2D(),
tf.keras.layers.Dense(6, activation='softmax')
])
learning_rate = 0.00001
model.compile(
loss='sparse_categorical_crossentropy',
optimizer=tf.keras.optimizers.Adam(learning_rate),
metrics=['accuracy']
)
return model |