File size: 1,799 Bytes
9ffe799 af1d27d 9ffe799 | 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 | from tensorflow.keras.models import Sequential
from 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 = 'Data/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((300, 300), Image.ANTIALIAS))
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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