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feat: adding resnet and formatting updates

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This commit is contained in:
Lucas Oskorep
2023-04-06 00:37:46 -04:00
parent ce5939d8a9
commit dc427837f6
12 changed files with 87 additions and 37 deletions
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4_train_keras_model.py
+57 -17
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@@ -53,7 +53,7 @@ def get_gen(path, dataset_type: DatasetType = DatasetType.TRAIN):
)
def train_model(model, model_name, train_gen, val_gen):
def train_model(model, model_name, train_gen, val_gen, max_epochs):
print(model)
print(f"NOW TRAINING: {model_name}")
checkpoint = keras.callbacks.ModelCheckpoint(
@@ -82,7 +82,7 @@ def train_model(model, model_name, train_gen, val_gen):
model.fit(
train_gen,
validation_data=val_gen,
epochs=100,
epochs=max_epochs,
batch_size=batch_size,
shuffle=True,
verbose=True,
@@ -130,7 +130,47 @@ if __name__ == "__main__":
pre_trained=True,
freeze_layers=True,
freeze_batch_norm=True,
base_model_type=ImageClassModels.EFFICIENTNET_V2S,
base_model_type=ImageClassModels.MOBILENET_V2,
dense_layer_neurons=1024,
dropout_rate=.5,
), ImageClassModelBuilder(
input_shape=input_shape,
n_classes=807,
optimizer=keras.optimizers.Adam(learning_rate=.0001),
pre_trained=True,
freeze_layers=True,
freeze_batch_norm=True,
base_model_type=ImageClassModels.INCEPTION_RESNET_V2,
dense_layer_neurons=1024,
dropout_rate=.5,
), ImageClassModelBuilder(
input_shape=input_shape,
n_classes=807,
optimizer=keras.optimizers.Adam(learning_rate=.0001),
pre_trained=True,
freeze_layers=True,
freeze_batch_norm=True,
base_model_type=ImageClassModels.INCEPTION_V3,
dense_layer_neurons=1024,
dropout_rate=.5,
), ImageClassModelBuilder(
input_shape=input_shape,
n_classes=807,
optimizer=keras.optimizers.Adam(learning_rate=.0001),
pre_trained=True,
freeze_layers=True,
freeze_batch_norm=True,
base_model_type=ImageClassModels.XCEPTION,
dense_layer_neurons=1024,
dropout_rate=.5,
), ImageClassModelBuilder(
input_shape=input_shape,
n_classes=807,
optimizer=keras.optimizers.Adam(learning_rate=.0001),
pre_trained=True,
freeze_layers=True,
freeze_batch_norm=True,
base_model_type=ImageClassModels.DENSENET201,
dense_layer_neurons=1024,
dropout_rate=.5,
)
@@ -141,17 +181,17 @@ if __name__ == "__main__":
train_gen = get_gen('./data/train', dataset_type=DatasetType.TRAIN)
val_gen = get_gen('./data/val', dataset_type=DatasetType.VAL)
test_gen = get_gen('./data/test', dataset_type=DatasetType.TEST)
model = train_model(model, model_name, train_gen, val_gen)
for layer in model.layers[2].layers:
if not isinstance(layer, keras.layers.BatchNormalization):
layer.trainable = True
model.layers[2].trainable = True
print(model)
model.compile(
optimizer=keras.optimizers.Adam(learning_rate=.00001),
loss=keras.losses.CategoricalCrossentropy(),
metrics=['accuracy', 'categorical_crossentropy']
)
model.summary()
model = train_model(model, model_name + "-second_stage", train_gen, val_gen)
test_model(model, test_gen)
model = train_model(model, model_name, train_gen, val_gen, 1)
# for layer in model.layers[2].layers:
# if not isinstance(layer, keras.layers.BatchNormalization):
# layer.trainable = True
# model.layers[2].trainable = True
# print(model)
# model.compile(
# optimizer=keras.optimizers.Adam(learning_rate=.00001),
# loss=keras.losses.CategoricalCrossentropy(),
# metrics=['accuracy', 'categorical_crossentropy']
# )
# model.summary()
# model = train_model(model, model_name + "-second_stage", train_gen, val_gen, 1)
# test_model(model, test_gen)