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How to use get_base_model method in dbt-osmosis

Best Python code snippet using dbt-osmosis_python

model.py

Source:model.py

2from abc import ABC, abstractmethod3class ImageBaseModel(ABC):4    @classmethod5    @abstractmethod6    def get_base_model(cls, h:int,w:int,c:int):7        """ãã¼ã¹ã¨ãªãã¢ãã«ãæä¾ããã8        9        Args:10            ImageBaseModel ([type]): [description]11            h (int): å¥åãµã¤ãº hight12            w (int): å¥åãµã¤ãº width13            c (int): å¥åãµã¤ãº channel14        15        Returns:16            [type]: CNNã¢ãã«17        """18        raise NotImplementedError()19class ClasificationModel(ABC):20    @classmethod21    @abstractmethod22    def get_classification_model(cls, base_model:tf.keras.Model)->tf.keras.Model:23        """ ãã¼ã¹ã¢ãã«ã«åé¡é¨åãã¤ããåé¡ç¨ã®ã¢ãã«ãæä¾ããã24        25        Args:26            base_model (tf.keras.Model): ãã¼ã¹ã¢ãã«27            classes (int): åé¡æ°28        29        Returns:30            tf.keras.Model: åé¡ã¢ãã«31        """32        raise NotImplementedError()33class SimpleCNN(ImageBaseModel):34    @classmethod35    def get_base_model(cls, h:int,w:int,c:int):36        """åç´ãªCNNã¢ãã«ãæä¾ããã37        38        Args:39            ImageBaseModel ([type]): [description]40            h (int): å¥åãµã¤ãº hight41            w (int): å¥åãµã¤ãº width42            c (int): å¥åãµã¤ãº channel43        44        Returns:45            [type]: CNNã¢ãã«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 CNNã¢ãã«ãæä¾ããã64        65        Args:66            ImageBaseModel ([type]): [description]67            h (int): å¥åãµã¤ãº hight68            w (int): å¥åãµã¤ãº width69            c (int): å¥åãµã¤ãº channel70        71        Returns:72            [type]: CNNã¢ãã«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        """ ãã¼ã¹ã¢ãã«ã«åé¡é¨åãã¤ããåé¡ç¨ã®ã¢ãã«ãæä¾ããã84        85        Args:86            base_model (tf.keras.Model): ãã¼ã¹ã¢ãã«87            classes (int): åé¡æ°88        89        Returns:90            tf.keras.Model: åé¡ã¢ãã«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)...

test.py

Source:test.py

...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__":...

test_base_model.py

Source:test_base_model.py

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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