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models.py
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models.py
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import tensorflow as tf
from typing import Tuple
def define_model_mlp(input_shape: Tuple[int, int]) -> tf.keras.Sequential:
model = tf.keras.Sequential(
[
tf.keras.layers.Flatten(
data_format="channels_last",
input_shape=input_shape,
name="flatten_layer"
),
tf.keras.layers.Dense(
units=512,
activation="relu",
kernel_regularizer=tf.keras.regularizers.l2(0.001),
name="dense_layer_1"
),
tf.keras.layers.Dense(
units=256,
activation="relu",
kernel_regularizer=tf.keras.regularizers.l2(0.001),
name="dense_layer_2"
),
tf.keras.layers.Dense(
units=128,
activation="relu",
kernel_regularizer=tf.keras.regularizers.l2(0.001),
name="dense_layer_3"
),
tf.keras.layers.Dense(
units=64,
activation="relu",
kernel_regularizer=tf.keras.regularizers.l2(0.001),
name="dense_layer_4"
),
tf.keras.layers.Dense(
units=10,
activation="softmax",
name="softmax_classifier"
),
],
name="svhn_classifier_mlp"
)
return model
def define_model_cnn(input_shape: Tuple[int, int, int]) -> tf.keras.Sequential:
model = tf.keras.Sequential(
[
tf.keras.layers.Conv2D(
filters=16,
kernel_size=(3, 3),
activation="relu",
padding="same",
input_shape=input_shape,
name="convolution_1",
),
tf.keras.layers.MaxPool2D(pool_size=(2, 2), name="max_pool_1"),
tf.keras.layers.BatchNormalization(),
tf.keras.layers.Dropout(0.5),
tf.keras.layers.Conv2D(
filters=8,
kernel_size=(3, 3),
activation="relu",
padding="same",
name="convolution_2",
),
tf.keras.layers.MaxPool2D(pool_size=(2, 2), name="max_pool_2"),
tf.keras.layers.BatchNormalization(),
tf.keras.layers.Dropout(0.5),
tf.keras.layers.Flatten(
data_format="channels_last",
name="flatten_layer"
),
tf.keras.layers.Dense(
units=256,
activation="relu",
kernel_regularizer=tf.keras.regularizers.l2(0.001),
name="dense_layer_1"
),
tf.keras.layers.Dropout(0.5),
tf.keras.layers.Dense(
units=128,
activation="relu",
kernel_regularizer=tf.keras.regularizers.l2(0.001),
name="dense_layer_2"
),
tf.keras.layers.Dropout(0.5),
tf.keras.layers.Dense(
units=10,
activation="softmax",
name="softmax_classifier"
),
],
name="svhn_classifier_cnn"
)
return model
def compile_model(model: tf.keras.Sequential) -> tf.keras.Sequential:
model.compile(
optimizer=tf.keras.optimizers.legacy.Adam(),
loss=tf.keras.losses.SparseCategoricalCrossentropy(),
metrics=["accuracy"]
)
return model