The Keras model for the object detection
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import keras | |
from keras.models import Sequential | |
from keras.utils import np_utils | |
from keras.preprocessing.image import ImageDataGenerator | |
from keras.layers import Dense, Activation, Flatten, Dropout, BatchNormalization | |
from keras.layers import Conv2D, MaxPooling2D | |
from keras.datasets import cifar10 | |
from keras import regularizers | |
from keras.callbacks import LearningRateScheduler | |
import numpy as np | |
# Create the model | |
model = Sequential() | |
model.add(Conv2D(32, (3, 3), padding='same', | |
input_shape=x_train.shape[1:])) | |
model.add(Activation('relu')) | |
model.add(Conv2D(32, (3, 3))) | |
model.add(Activation('relu')) | |
model.add(MaxPooling2D(pool_size=(2, 2))) | |
model.add(Dropout(0.25)) | |
model.add(Conv2D(64, (3, 3), padding='same')) | |
model.add(Activation('relu')) | |
model.add(Conv2D(64, (3, 3))) | |
model.add(Activation('relu')) | |
model.add(MaxPooling2D(pool_size=(2, 2))) | |
model.add(Dropout(0.25)) | |
model.add(Flatten()) | |
model.add(Dense(512)) | |
model.add(Activation('relu')) | |
model.add(Dropout(0.5)) | |
model.add(Dense(num_classes)) | |
model.add(Activation('softmax')) |
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