65 lines
1.9 KiB
Python
65 lines
1.9 KiB
Python
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#!/usr/bin/python3
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from tensorflow.keras.preprocessing.image import ImageDataGenerator
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img_size = (150, 150)
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batch_size = 128
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extract_path="../ds/raw-img"
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datagen = ImageDataGenerator(
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rescale=1.0/255,
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validation_split=0.2
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)
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def __dg(subset):
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return datagen.flow_from_directory(extract_path,
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target_size = img_size,
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batch_size = batch_size,
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class_mode = "categorical",
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subset = subset,
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shuffle = True)
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train_generator = __dg("training")
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val_generator = __dg("validation")
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from tensorflow.keras import models as m
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from tensorflow.keras import layers as l
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from tensorflow.keras import optimizers as o
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model = m.Sequential([
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l.Input(shape=(150, 150, 3)),
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l.Conv2D(96, (11, 11), strides=4, activation='relu'),
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l.BatchNormalization(),
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l.MaxPooling2D((3, 3), strides=2),
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l.Conv2D(192, (5, 5), activation='relu', padding='same'),
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l.BatchNormalization(),
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l.MaxPooling2D((3, 3), strides=2),
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l.Conv2D(256, (3, 3), activation='relu', padding='same'),
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l.Conv2D(256, (3, 3), activation='relu', padding='same'),
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l.Conv2D(160, (3, 3), activation='relu', padding='same'),
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l.BatchNormalization(),
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l.MaxPooling2D((3, 3), strides=2),
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l.Flatten(),
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l.Dense(1024, activation='relu'),
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l.Dropout(0.5),
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l.Dense(1024, activation='relu'),
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l.Dropout(0.5),
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l.Dense(10, activation='softmax'),
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])
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model.compile(optimizer = o.Adam(learning_rate = 0.0001),
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loss = 'categorical_crossentropy',
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metrics = ['accuracy'])
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print(model.summary())
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model.load_weights("w2.weights.h5")
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model.fit(train_generator,
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epochs = 10,
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validation_data = val_generator)
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model.save_weights("w2.weights.h5")
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