How to use get_base_model method in dbt-osmosis

Best Python code snippet using dbt-osmosis_python

model.py

Source: model.py Github

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2from abc import ABC, abstractmethod3class ImageBaseModel(ABC):4 @classmethod5 @abstractmethod6 def get_base_model(cls, h:int,w:int,c:int):7 """ベースとなるモデルを提供する。8 9 Args:10 ImageBaseModel ([type]): [description]11 h (int): 入力サイズ hight12 w (int): 入力サイズ width13 c (int): 入力サイズ channel14 15 Returns:16 [type]: CNNモデル17 """18 raise NotImplementedError()19class ClasificationModel(ABC):20 @classmethod21 @abstractmethod22 def get_classification_model(cls, base_model:tf.keras.Model)->tf.keras.Model:23 """ ベースモデルに分類部分をつけた分類用のモデルを提供する。24 25 Args:26 base_model (tf.keras.Model): ベースモデル27 classes (int): 分類数28 29 Returns:30 tf.keras.Model: 分類モデル31 """32 raise NotImplementedError()33class SimpleCNN(ImageBaseModel):34 @classmethod35 def get_base_model(cls, h:int,w:int,c:int):36 """単純なCNNモデルを提供する。37 38 Args:39 ImageBaseModel ([type]): [description]40 h (int): 入力サイズ hight41 w (int): 入力サイズ width42 c (int): 入力サイズ channel43 44 Returns:45 [type]: CNNモデル46 """47 input_layer = tf.keras.Input(shape=(h,w,c),name='input',dtype=tf.float32)48 x = tf.keras.layers.Conv2D(32, (3, 3), padding='same', name='conv2d-1')(input_layer)49 x = tf.keras.layers.Activation('relu')(x)50 x = tf.keras.layers.Conv2D(32, (3, 3), padding='same', name='conv2d-2')(x)51 x = tf.keras.layers.Activation('relu')(x)52 x = tf.keras.layers.MaxPooling2D(pool_size=(2, 2),name='maxpool-1')(x)53 x = tf.keras.layers.Dropout(0.25)(x)54 x = tf.keras.layers.Conv2D(32, (3, 3), padding='same', name='conv2d-3')(x)55 x = tf.keras.layers.Activation('relu')(x)56 x = tf.keras.layers.Conv2D(32, (3, 3), padding='same', name='conv2d-4')(x)57 x = tf.keras.layers.Activation('relu')(x)58 x = tf.keras.layers.MaxPooling2D(pool_size=(2, 2),name='maxpool-2')(x)59 return tf.keras.Model(input_layer,x)60class ResNet50(ImageBaseModel):61 @classmethod62 def get_base_model(cls, h:int,w:int,c:int, weights:bool=False):63 """ResNet CNNモデルを提供する。64 65 Args:66 ImageBaseModel ([type]): [description]67 h (int): 入力サイズ hight68 w (int): 入力サイズ width69 c (int): 入力サイズ channel70 71 Returns:72 [type]: CNNモデル73 """74 if weights==False:75 w_imagenet = None76 else:77 w_imagenet = "imagenet"78 model = tf.keras.applications.ResNet50(include_top=False,input_shape=(h,w,c),weights=w_imagenet)79 return model80class SimpleSoftmaxClassificationModel(ClasificationModel):81 @classmethod82 def get_classification_model(cls, base_model:tf.keras.Model,classes:int)->tf.keras.Model:83 """ ベースモデルに分類部分をつけた分類用のモデルを提供する。84 85 Args:86 base_model (tf.keras.Model): ベースモデル87 classes (int): 分類数88 89 Returns:90 tf.keras.Model: 分類モデル91 """92 x = tf.keras.layers.Flatten()(base_model.output)93 x = tf.keras.layers.Activation('relu')(x)94 x = tf.keras.layers.Dense(100)(x)95 x = tf.keras.layers.Activation('relu')(x)96 x = tf.keras.layers.Dense(classes, activation='softmax')(x)97 return tf.keras.Model(base_model.input, x)98if __name__ == '__main__':99 base = SimpleCNN.get_base_model(28,28,1)100 model = SimpleSoftmaxClassificationModel.get_classification_model(base,10)...

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test.py

Source: test.py Github

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...51 model.fit(x=x, y=y, epochs=5, batch_size=4, validation_split=0.1, verbose=0)52 def test_model_dsc(self):53 x = np.array([[5, 2, 3] * 3] * 100)54 y = np.array([[1, 2, 3] * 3] * 100)55 base_model = get_base_model(9)56 model = ModelWithCRFLossDSCLoss(base_model)57 model.compile(optimizer='adam')58 model.fit(x=x, y=y, epochs=5, batch_size=4, validation_split=0.1, verbose=0)59 def test_mixed_precison_dsc(self):60 from tensorflow.keras.mixed_precision import experimental as mixed_precision61 x = np.array([[5, 2, 3] * 3] * 100)62 y = np.array([[1, 2, 3] * 3] * 100)63 policy = mixed_precision.Policy('mixed_float16')64 mixed_precision.set_policy(policy)65 base_model = get_base_model(9)66 model = ModelWithCRFLossDSCLoss(base_model)67 model.compile(optimizer='adam')68 model.fit(x=x, y=y, epochs=5, batch_size=4, validation_split=0.1, verbose=0)69def get_model(units: int):70 base_model = get_base_model(units)71 model = ModelWithCRFLoss(base_model)72 model.compile(optimizer='adam')73 return model74def get_base_model(units: int):75 inputs = Input(shape=(None,), dtype='int32')76 output = Embedding(10, 20, trainable=True, mask_zero=True)(inputs)77 crf = CRF(units=units, dtype='float32', name='crf')78 output = crf(output)79 base_model = Model(inputs=inputs, outputs=output)80 return base_model81if __name__ == "__main__":...

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test_base_model.py

Source: test_base_model.py Github

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1import pytest2from model.base_model import BaseModel3def get_base_model(problem):4 return BaseModel("test", problem)5@pytest.mark.parametrize("problem_name, expected", [("rproblem2", [0]), ("rproblem3", [0])])6def test_model_competencies(problem_name, expected):7 base_model = get_base_model(problem_name)8 assert base_model.competencies == expected9@pytest.mark.parametrize(10 "problem_name, expected_length, expected_start",11 [("rproblem2", 1170, [8.0, 8.5, 9, 9.5]), ("rproblem3", 1176, [7.75, 8.0, 8.25, 8.5])],12)13def test_model_time_periods(problem_name, expected_length, expected_start):14 base_model = get_base_model(problem_name)15 time_periods = base_model.time_set["periods"][0]16 assert len(time_periods) == expected_length17 assert time_periods[:4] == expected_start18@pytest.mark.parametrize(19 "problem_name, expected_length, expected_start",20 [("rproblem2", 10, [8.0, 8.5, 9, 9.5]), ("rproblem3", 4, [7.75, 8.0, 8.25, 8.5])],21)22def test_model_time_periods_in_week(problem_name, expected_length, expected_start):23 base_model = get_base_model(problem_name)24 time_periods = base_model.time_set["periods"][1]25 assert len(time_periods) == expected_length26 assert time_periods[0][:4] == expected_start27def test_get_variables_returns_none():28 """ The base model should not contain any variables """29 base_model = get_base_model("problem12")...

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