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Dog Breed - Simple CNN
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from keras.layers import Conv2D, MaxPooling2D, GlobalAveragePooling2D | |
from keras.layers import Dropout, Flatten, Dense | |
from keras.models import Sequential | |
model = Sequential() | |
### TODO: Define your architecture. | |
# image is 224×224x3 pixels | |
model.add(Conv2D(filters=16, kernel_size=(2,2), activation='relu', input_shape=(224,224,3))) | |
model.add(MaxPooling2D(pool_size=(2, 2), strides=None, padding='same', data_format=None)) | |
model.add(Conv2D(filters=32, kernel_size=(2,2), activation='relu')) | |
model.add(MaxPooling2D(pool_size=(2, 2), strides=None, padding='same', data_format=None)) | |
model.add(Conv2D(filters=64, kernel_size=(2,2), activation='relu')) | |
model.add(MaxPooling2D(pool_size=(2, 2), strides=None, padding='same', data_format=None)) | |
model.add(GlobalAveragePooling2D(data_format=None)) | |
model.add(Dense(units=133, activation='softmax')) | |
model.summary() |
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