c4v/model.py at main · lrustand/c4v · GitHub

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import tensorflow as tf
from tensorflow.keras import layers, models


def model(out_size):
    model = models.Sequential()
    model.add(layers.Conv2D(32, (3, 3), activation='relu',
                            input_shape=(32, 32, 3)))
    model.add(layers.MaxPooling2D((2, 2)))
    model.add(layers.Conv2D(64, (3, 3), activation='relu'))
    model.add(layers.MaxPooling2D((2, 2)))
    model.add(layers.Conv2D(64, (3, 3), activation='relu'))

    model.add(layers.Flatten())
    model.add(layers.Dense(32, activation='relu'))
    model.add(layers.Dense(out_size))

    model.summary()

    model.compile(optimizer='adam',
                  loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
                  metrics=['accuracy'])
    return model

def mini_model(out_size):
    model = models.Sequential()
    model.add(layers.Conv2D(32, (3, 3), activation='relu',
                            input_shape=(32, 32, 3)))
    model.add(layers.MaxPooling2D((2, 2)))
    model.add(layers.Conv2D(64, (3, 3), activation='relu'))
    model.add(layers.MaxPooling2D((2, 2)))
    model.add(layers.Conv2D(64, (3, 3), activation='relu'))

    model.add(layers.Flatten())
    model.add(layers.Dense(8, activation='relu'))
    model.add(layers.Dense(out_size))

    model.summary()

    model.compile(optimizer='adam',
                  loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
                  metrics=['accuracy'])
    return model