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5974209
1
Parent(s):
97ea680
Update utils.py
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utils.py
CHANGED
@@ -1,24 +1,3 @@
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from tensorflow.keras.models import Sequential
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from tensorflow.keras.layers import Conv2D, Flatten, MaxPooling2D, Dense, Dropout, SpatialDropout2D
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from tensorflow.keras.losses import sparse_categorical_crossentropy, binary_crossentropy
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from tensorflow.keras.optimizers import Adam
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from tensorflow.keras.preprocessing.image import ImageDataGenerator
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import numpy as np
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from PIL import Image
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def gen_labels():
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train = 'Dataset/Train'
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train_generator = ImageDataGenerator(rescale = 1/255)
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train_generator = train_generator.flow_from_directory(train,
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target_size = (300,300),
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batch_size = 32,
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class_mode = 'sparse')
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labels = (train_generator.class_indices)
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labels = dict((v,k) for k,v in labels.items())
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return labels
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def preprocess(image):
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image = np.array(image.resize((256, 256), Image.LANCZOS))
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image = np.array(image, dtype='uint8')
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model = Sequential()
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# Convolution blocks
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model.add(Conv2D(32, kernel_size=(3,3), padding='same', input_shape=(
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model.add(MaxPooling2D(pool_size=2))
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model.add(Conv2D(64, kernel_size=(3,3), padding='same', activation='relu'))
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@@ -49,4 +28,4 @@ def model_arc():
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model.add(Dropout(0.2))
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model.add(Dense(6, activation='softmax'))
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return model
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def preprocess(image):
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image = np.array(image.resize((256, 256), Image.LANCZOS))
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image = np.array(image, dtype='uint8')
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model = Sequential()
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# Convolution blocks
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model.add(Conv2D(32, kernel_size=(3,3), padding='same', input_shape=(256, 256, 3), activation='relu'))
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model.add(MaxPooling2D(pool_size=2))
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model.add(Conv2D(64, kernel_size=(3,3), padding='same', activation='relu'))
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model.add(Dropout(0.2))
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model.add(Dense(6, activation='softmax'))
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return model
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