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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 = (300,300),
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 = np.array(image, dtype='uint8')
image = np.array(image) / 255.0
return image
def model_arc():
model = Sequential()
# Convolution blocks
model.add(Conv2D(32, kernel_size=(3,3), padding='same', input_shape=(300,300,3), activation='relu'))
model.add(MaxPooling2D(pool_size=2))
model.add(Conv2D(64, kernel_size=(3,3), padding='same', activation='relu'))
model.add(MaxPooling2D(pool_size=2))
model.add(Conv2D(32, kernel_size=(3,3), padding='same', activation='relu'))
model.add(MaxPooling2D(pool_size=2))
# Classification layers
model.add(Flatten())
model.add(Dense(64, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(32, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(6, activation='softmax'))
return model
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