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vvg_like.py
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vvg_like.py
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import numpy as np
import keras
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras.optimizers import Adam
""" Priprava naključnih podatkov """
x_train = np.random.random((100, 100, 100, 3))
y_train = keras.utils.to_categorical(
np.random.randint(10, size=(100, 1)), num_classes=10)
x_validation = np.random.random((20, 100, 100, 3))
y_validation = keras.utils.to_categorical(
np.random.randint(10, size=(20, 1)), num_classes=10)
x_test = np.random.random((20, 100, 100, 3))
""" Arhitektura modela """
model = Sequential()
model.add(
Conv2D(32, (3, 3), activation='relu', input_shape=(100, 100, 3)))
model.add(Conv2D(32, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(256, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(10, activation='softmax'))
""" Učenje modela """
adam = Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-08)
model.compile(loss='categorical_crossentropy', optimizer=adam)
model.fit(x_train, y_train, batch_size=32, epochs=10)
""" Validacija modela """
score = model.evaluate(x_test, y_test, batch_size=32)