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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
def preprocess(image):
image = np.array(image.resize((256, 256), Image.LANCZOS))
image = image.astype('float32') / 255.0
return image
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 |