Python keras.Model() Examples

def get_custom_architecture(name, trainings_dir, output_layer):
    from keras.models import load_model, Model
    name = name.lstrip("@")
    model = load_model(os.path.join(trainings_dir, name, 'checkpoints', 'model.h5'))
    try:
        if isinstance(output_layer, int):
            layer = model.layers[output_layer]
        else:
            layer = model.get_layer(output_layer)
    except Exception:
        if isinstance(output_layer, int):
            raise VergeMLError(f'output-layer {output_layer} not found - model has only {len(model.layers)} layers.')
        else:
            candidates = list(map(lambda l: l.name, model.layers))
            raise VergeMLError(f'output-layer named {output_layer} not found.',
                               suggestion=did_you_mean(candidates, output_layer))
    model = Model(inputs=model.input, outputs=layer.output)
    return model 

def load_openai_transformer(path: str = './openai/model/', use_attn_mask: bool = True,
                            use_one_embedding_dropout: bool = False, max_len: int = 512) -> keras.Model:
    with open(path + 'params_shapes.json') as f:
        shapes = json.load(f)
    offsets = np.cumsum([np.prod(shape) for shape in shapes])
    init_params = [np.load(path + 'params_{}.npy'.format(n)) for n in range(10)]
    init_params = np.split(np.concatenate(init_params, 0), offsets)[:-1]
    init_params = [param.reshape(shape) for param, shape in zip(init_params, shapes)]
    init_params[0] = init_params[0][:min(512, max_len)]
    # add special token embedding to token embedding
    init_params[1] = np.concatenate(
        (init_params[1], np.random.randn(TextEncoder.SPECIAL_COUNT, 768).astype(np.float32) * 0.02), axis=0)
    init_params = [np.zeros((TextEncoder.NUM_SEGMENTS, 768)).astype(np.float32)] + init_params  # segment embedding
    model = create_transformer(embedding_dim=768, embedding_dropout=0.1, vocab_size=40478,
                               max_len=min(512, max_len), use_attn_mask=use_attn_mask, trainable_pos_embedding=True,
                               num_heads=12, num_layers=12, use_one_embedding_dropout=use_one_embedding_dropout,
                               d_hid=4 * 768, attention_dropout=0.1, residual_dropout=0.1)
    model.set_weights(init_params)
    return model 

def create_transformer(embedding_dim: int = 768, embedding_dropout: float = 0.1,
                       vocab_size: int = 30000, max_len: int = 512,
                       trainable_pos_embedding: bool = True, num_heads: int = 12, num_layers: int = 12,
                       attention_dropout: float = 0.1, use_one_embedding_dropout: bool = False,
                       d_hid: int = 768 * 4, residual_dropout: float = 0.1,
                       use_attn_mask: bool = True) -> keras.Model:
    vocab_size += TextEncoder.SPECIAL_COUNT
    tokens = Input(batch_shape=(None, max_len), name='token_input', dtype='int32')
    segment_ids = Input(batch_shape=(None, max_len), name='segment_input', dtype='int32')
    pos_ids = Input(batch_shape=(None, max_len), name='position_input', dtype='int32')
    attn_mask = Input(batch_shape=(None, 1, max_len, max_len), name='attention_mask_input',
                      dtype=K.floatx()) if use_attn_mask else None
    inputs = [tokens, segment_ids, pos_ids]
    embedding_layer = Embedding(embedding_dim, embedding_dropout, vocab_size, max_len, trainable_pos_embedding,
                                use_one_embedding_dropout)
    x = embedding_layer(inputs)
    for i in range(num_layers):
        x = EncoderLayer(embedding_dim, num_heads, d_hid, residual_dropout,
                         attention_dropout, use_attn_mask, i)(x, attn_mask)
    inputs = inputs + ([attn_mask] if use_attn_mask else [])
    return keras.Model(inputs=inputs, outputs=x, name='Transformer') 

def create_transformer(embedding_dim: int = 768, embedding_dropout: float = 0.1, vocab_size: int = 30000,
                       max_len: int = 512, trainable_pos_embedding: bool = True, num_heads: int = 12,
                       num_layers: int = 12, attention_dropout: float = 0.1, use_one_embedding_dropout: bool = False,
                       d_hid: int = 768 * 4, residual_dropout: float = 0.1, use_attn_mask: bool = True,
                       embedding_layer_norm: bool = False, neg_inf: float = -1e9, layer_norm_epsilon: float = 1e-5,
                       accurate_gelu: bool = False) -> keras.Model:
    vocab_size += TextEncoder.SPECIAL_COUNT
    tokens = Input(batch_shape=(None, max_len), name='token_input', dtype='int32')
    segment_ids = Input(batch_shape=(None, max_len), name='segment_input', dtype='int32')
    pos_ids = Input(batch_shape=(None, max_len), name='position_input', dtype='int32')
    attn_mask = Input(batch_shape=(None, 1, max_len, max_len), name='attention_mask_input',
                      dtype=K.floatx()) if use_attn_mask else None
    inputs = [tokens, segment_ids, pos_ids]
    embedding_layer = Embedding(embedding_dim, embedding_dropout, vocab_size, max_len, trainable_pos_embedding,
                                use_one_embedding_dropout, embedding_layer_norm, layer_norm_epsilon)
    x = embedding_layer(inputs)
    for i in range(num_layers):
        x = EncoderLayer(embedding_dim, num_heads, d_hid, residual_dropout,
                         attention_dropout, use_attn_mask, i, neg_inf, layer_norm_epsilon, accurate_gelu)(x, attn_mask)
    if use_attn_mask:
        inputs.append(attn_mask)
    return keras.Model(inputs=inputs, outputs=[x], name='Transformer') 

def load_openai_transformer(path: str = './openai/model/', use_attn_mask: bool = True,
                            use_one_embedding_dropout: bool = False, max_len: int = 512) -> keras.Model:
    with open(path + 'params_shapes.json') as f:
        shapes = json.load(f)
    offsets = np.cumsum([np.prod(shape) for shape in shapes])
    init_params = [np.load(path + 'params_{}.npy'.format(n)) for n in range(10)]
    init_params = np.split(np.concatenate(init_params, 0), offsets)[:-1]
    init_params = [param.reshape(shape) for param, shape in zip(init_params, shapes)]
    init_params[0] = init_params[0][:min(512, max_len)]
    # add special token embedding to token embedding
    init_params[1] = np.concatenate(
        (init_params[1], np.random.randn(TextEncoder.SPECIAL_COUNT, 768).astype(np.float32) * 0.02), axis=0)
    init_params = [np.zeros((TextEncoder.NUM_SEGMENTS, 768)).astype(np.float32)] + init_params  # segment embedding
    model = create_transformer(embedding_dim=768, embedding_dropout=0.1, vocab_size=40478,
                               max_len=min(512, max_len), use_attn_mask=use_attn_mask, trainable_pos_embedding=True,
                               num_heads=12, num_layers=12, use_one_embedding_dropout=use_one_embedding_dropout,
                               d_hid=4 * 768, attention_dropout=0.1, residual_dropout=0.1)
    model.set_weights(init_params)
    return model 

def build_model(self, input_shape, horizon, conditions_shape=None):
        """
        Create a Model that takes as inputs:
            - 3D tensor of shape tesor (batch_size, window_size, n_features)
            - 3D Tensor of shape (batch_size, window_size, n_features)
        and outputs:
            - 2D tensor of shape (batch_size, 1) or (batch_size, horizon), depending on the value of
            recursive_forecast.
        :param input_shape: np.array
            (window_size, n_features)
        :param horizon: int
            the forecasting horizon
        :param conditions_shape: np.array
            (horizon, n_features)
        :return: a keras Model
        """
        pass 

def build_model(self, input_shape, horizon, conditions_shape=None):
        self.horizon = horizon
        model_inputs, inputs = self.model_inputs(input_shape, conditions_shape)
        out = Flatten()(inputs)
        for units in self.layers:
            out = Dense(units=units, kernel_regularizer=self.kernel_regularizer, activation=self.activation,
                        kernel_initializer=self.kernel_initializer, kernel_constraint=self.kernel_constraint,
                        use_bias=self.use_bias, bias_regularizer=self.bias_regularizer,
                        bias_initializer=self.bias_initializer, bias_constraint=self.bias_constraint)(out)
            out = Dropout(self.dropout)(out)
        if self.recursive_forecast:
            out = Dense(units=1, activation='linear')(out)
        else:
            out = Dense(units=self.horizon, activation='linear')(out)
        self.model = Model(model_inputs, out)
        self.model.summary()
        return self.model 

def build_model(self, input_shape, horizon):
        """
        Create a Model that takes as inputs:
            - 3D Tensor of shape (batch_size, window_size, n_features)
        and outputs:
            - 2D tensor of shape (batch_size, 1)

        :param input_shape:
            (window_size, n_features)
        :param horizon: int
            The forecasting horizon
        :return: a keras Model
        """
        self.horizon = horizon
        if len(input_shape) < 2:
            input_shape = (input_shape[0], 1)
        inputs = Input(shape=input_shape, dtype='float32')
        out_rnn = self.rnn(inputs)                    # [batch_size, hidden_state_length]
        outputs = Dense(1, activation=None)(out_rnn)  # [batch_size, 1]

        self.model = Model(inputs=[inputs], outputs=[outputs])
        self.model.summary()
        return self.model 

def __init__(
        self,
        model: Model,
        dataset,
        noise_stepsize: float = 0.01,
        batch_size: int = 31,
        decay: float = 0,
    ):
        super(KerasDNN, self).__init__()
        self.model = model
        self.dataset = dataset
        self.batch_size = batch_size
        self.noise_stepsize = noise_stepsize
        self.noise_decay = decay
        (self.X_train, self.y_train), (self.X_test, self.y_test) = dataset.load_data()
        self.loss = None
        self.metric = None
        self.X = None
        self.y = None 

def load_optimizer_weights(model: Model, model_save_path: str):
    """
    Loads optimizer's weights for the model from an HDF5 file.
    """
    with h5py.File(model_save_path, mode='r') as f:
        if 'optimizer_weights' in f:
            # Build train function (to get weight updates).
            # noinspection PyProtectedMember
            model._make_train_function()
            optimizer_weights_group = f['optimizer_weights']
            optimizer_weight_names = [
                n.decode('utf8') for n in
                optimizer_weights_group.attrs['weight_names']]
            optimizer_weight_values = [
                optimizer_weights_group[n]
                for n in optimizer_weight_names]
            try:
                model.optimizer.set_weights(optimizer_weight_values)
            except ValueError:
                warnings.warn('Error in loading the saved optimizer '
                              'state. As a result, your model is '
                              'starting with a freshly initialized '
                              'optimizer.') 

def test_whole(self):
        # Create datatype
        datatype = Categorical()

        # Load observations
        observations = lib.load_mushroom()

        # Transform observations
        mapper = DataFrameMapper([(['cap-shape'], datatype.default_transformation_pipeline)], df_out=True)
        transformed_df = mapper.fit_transform(observations)

        # Create network
        input_layer, input_nub = datatype.input_nub_generator('cap-shape', transformed_df)
        output_nub = datatype.output_nub_generator('cap-shape', transformed_df)

        x = input_nub
        x = output_nub(x)

        model = Model(input_layer, x)
        model.compile(optimizer='adam', loss=datatype.output_suggested_loss()) 

def test_whole(self):
        datatype = Text()

        # Load observations
        observations = lib.load_titanic()

        # Transform observations
        mapper = DataFrameMapper([(['name'], datatype.default_transformation_pipeline),
                                  (['fare'], None)], df_out=True)
        transformed_df = mapper.fit_transform(observations)

        # Create network
        input_layer, input_nub = datatype.input_nub_generator('name', transformed_df)
        output_nub = Dense(1)

        x = input_nub
        x = output_nub(x)

        model = Model(input_layer, x)
        model.compile(optimizer='adam', loss='mse')
        pass 

def test_whole(self):
        # Create datatype
        datatype = Numerical()

        # Load observations
        observations = lib.load_titanic()

        # Transform observations
        mapper = DataFrameMapper([(['fare'], datatype.default_transformation_pipeline)], df_out=True)
        transformed_df = mapper.fit_transform(observations)

        # Create network
        input_layer, input_nub = datatype.input_nub_generator('fare', transformed_df)
        output_nub = datatype.output_nub_generator('fare', transformed_df)

        x = input_nub
        x = output_nub(x)

        model = Model(input_layer, x)
        model.compile(optimizer='adam', loss=datatype.output_suggested_loss()) 

def __build_model(self, emb_matrix=None):
        word_input = Input(shape=(None,), dtype='int32', name="word_input")

        word_emb = Embedding(self.vocab_size + 1, self.embed_dim,
                             weights=[emb_matrix] if emb_matrix is not None else None,
                             trainable=True if emb_matrix is None else False,
                             name='word_emb')(word_input)

        bilstm_output = Bidirectional(LSTM(self.bi_lstm_units // 2,
                                           return_sequences=True))(word_emb)

        bilstm_output = Dropout(self.dropout_rate)(bilstm_output)

        output = Dense(self.chunk_size + 1, kernel_initializer="he_normal")(bilstm_output)
        output = CRF(self.chunk_size + 1, sparse_target=self.sparse_target)(output)

        model = Model([word_input], [output])
        parallel_model = model
        if self.num_gpu > 1:
            parallel_model = multi_gpu_model(model, gpus=self.num_gpu)

        parallel_model.compile(optimizer=self.optimizer, loss=crf_loss, metrics=[crf_accuracy])
        return model, parallel_model 

def test_return_state():
    input_size = 10
    timesteps = 6
    units = 2
    num_samples = 32

    for layer_class in [keras.layers.CuDNNGRU, keras.layers.CuDNNLSTM]:
        num_states = 2 if layer_class is keras.layers.CuDNNLSTM else 1

        inputs = keras.Input(batch_shape=(num_samples, timesteps, input_size))
        layer = layer_class(units, return_state=True, stateful=True)
        outputs = layer(inputs)
        output, state = outputs[0], outputs[1:]
        assert len(state) == num_states
        model = keras.models.Model(inputs, state[0])

        inputs = np.random.random((num_samples, timesteps, input_size))
        state = model.predict(inputs)
        np.testing.assert_allclose(
            keras.backend.eval(layer.states[0]), state, atol=1e-4) 

def test_specify_initial_state_keras_tensor():
    input_size = 10
    timesteps = 6
    units = 2
    num_samples = 32
    for layer_class in [keras.layers.CuDNNGRU, keras.layers.CuDNNLSTM]:
        num_states = 2 if layer_class is keras.layers.CuDNNLSTM else 1

        inputs = keras.Input((timesteps, input_size))
        initial_state = [keras.Input((units,)) for _ in range(num_states)]
        layer = layer_class(units)
        if len(initial_state) == 1:
            output = layer(inputs, initial_state=initial_state[0])
        else:
            output = layer(inputs, initial_state=initial_state)
        assert initial_state[0] in layer._inbound_nodes[0].input_tensors

        model = keras.models.Model([inputs] + initial_state, output)
        model.compile(loss='categorical_crossentropy', optimizer='adam')

        inputs = np.random.random((num_samples, timesteps, input_size))
        initial_state = [np.random.random((num_samples, units))
                         for _ in range(num_states)]
        targets = np.random.random((num_samples, units))
        model.fit([inputs] + initial_state, targets) 

def test_specify_initial_state_keras_tensor():
    input_size = 10
    timesteps = 6
    units = 2
    num_samples = 32
    for layer_class in [keras.layers.CuDNNGRU, keras.layers.CuDNNLSTM]:
        num_states = 2 if layer_class is keras.layers.CuDNNLSTM else 1

        inputs = keras.Input((timesteps, input_size))
        initial_state = [keras.Input((units,)) for _ in range(num_states)]
        layer = layer_class(units)
        if len(initial_state) == 1:
            output = layer(inputs, initial_state=initial_state[0])
        else:
            output = layer(inputs, initial_state=initial_state)
        assert initial_state[0] in layer._inbound_nodes[0].input_tensors

        model = keras.models.Model([inputs] + initial_state, output)
        model.compile(loss='categorical_crossentropy', optimizer='adam')

        inputs = np.random.random((num_samples, timesteps, input_size))
        initial_state = [np.random.random((num_samples, units))
                         for _ in range(num_states)]
        targets = np.random.random((num_samples, units))
        model.fit([inputs] + initial_state, targets) 

def test_return_state():
    input_size = 10
    timesteps = 6
    units = 2
    num_samples = 32

    for layer_class in [keras.layers.CuDNNGRU, keras.layers.CuDNNLSTM]:
        num_states = 2 if layer_class is keras.layers.CuDNNLSTM else 1

        inputs = keras.Input(batch_shape=(num_samples, timesteps, input_size))
        layer = layer_class(units, return_state=True, stateful=True)
        outputs = layer(inputs)
        output, state = outputs[0], outputs[1:]
        assert len(state) == num_states
        model = keras.models.Model(inputs, state[0])

        inputs = np.random.random((num_samples, timesteps, input_size))
        state = model.predict(inputs)
        np.testing.assert_allclose(
            keras.backend.eval(layer.states[0]), state, atol=1e-4) 

def test_specify_initial_state_keras_tensor():
    input_size = 10
    timesteps = 6
    units = 2
    num_samples = 32
    for layer_class in [keras.layers.CuDNNGRU, keras.layers.CuDNNLSTM]:
        num_states = 2 if layer_class is keras.layers.CuDNNLSTM else 1

        inputs = keras.Input((timesteps, input_size))
        initial_state = [keras.Input((units,)) for _ in range(num_states)]
        layer = layer_class(units)
        if len(initial_state) == 1:
            output = layer(inputs, initial_state=initial_state[0])
        else:
            output = layer(inputs, initial_state=initial_state)
        assert initial_state[0] in layer._inbound_nodes[0].input_tensors

        model = keras.models.Model([inputs] + initial_state, output)
        model.compile(loss='categorical_crossentropy', optimizer='adam')

        inputs = np.random.random((num_samples, timesteps, input_size))
        initial_state = [np.random.random((num_samples, units))
                         for _ in range(num_states)]
        targets = np.random.random((num_samples, units))
        model.fit([inputs] + initial_state, targets) 

def test_return_state():
    input_size = 10
    timesteps = 6
    units = 2
    num_samples = 32

    for layer_class in [keras.layers.CuDNNGRU, keras.layers.CuDNNLSTM]:
        num_states = 2 if layer_class is keras.layers.CuDNNLSTM else 1

        inputs = keras.Input(batch_shape=(num_samples, timesteps, input_size))
        layer = layer_class(units, return_state=True, stateful=True)
        outputs = layer(inputs)
        output, state = outputs[0], outputs[1:]
        assert len(state) == num_states
        model = keras.models.Model(inputs, state[0])

        inputs = np.random.random((num_samples, timesteps, input_size))
        state = model.predict(inputs)
        np.testing.assert_allclose(
            keras.backend.eval(layer.states[0]), state, atol=1e-4) 

def test_specify_initial_state_keras_tensor():
    input_size = 10
    timesteps = 6
    units = 2
    num_samples = 32
    for layer_class in [keras.layers.CuDNNGRU, keras.layers.CuDNNLSTM]:
        num_states = 2 if layer_class is keras.layers.CuDNNLSTM else 1

        inputs = keras.Input((timesteps, input_size))
        initial_state = [keras.Input((units,)) for _ in range(num_states)]
        layer = layer_class(units)
        if len(initial_state) == 1:
            output = layer(inputs, initial_state=initial_state[0])
        else:
            output = layer(inputs, initial_state=initial_state)
        assert initial_state[0] in layer._inbound_nodes[0].input_tensors

        model = keras.models.Model([inputs] + initial_state, output)
        model.compile(loss='categorical_crossentropy', optimizer='adam')

        inputs = np.random.random((num_samples, timesteps, input_size))
        initial_state = [np.random.random((num_samples, units))
                         for _ in range(num_states)]
        targets = np.random.random((num_samples, units))
        model.fit([inputs] + initial_state, targets) 

def test_specify_initial_state_keras_tensor():
    input_size = 10
    timesteps = 6
    units = 2
    num_samples = 32
    for layer_class in [keras.layers.CuDNNGRU, keras.layers.CuDNNLSTM]:
        num_states = 2 if layer_class is keras.layers.CuDNNLSTM else 1

        inputs = keras.Input((timesteps, input_size))
        initial_state = [keras.Input((units,)) for _ in range(num_states)]
        layer = layer_class(units)
        if len(initial_state) == 1:
            output = layer(inputs, initial_state=initial_state[0])
        else:
            output = layer(inputs, initial_state=initial_state)
        assert initial_state[0] in layer._inbound_nodes[0].input_tensors

        model = keras.models.Model([inputs] + initial_state, output)
        model.compile(loss='categorical_crossentropy', optimizer='adam')

        inputs = np.random.random((num_samples, timesteps, input_size))
        initial_state = [np.random.random((num_samples, units))
                         for _ in range(num_states)]
        targets = np.random.random((num_samples, units))
        model.fit([inputs] + initial_state, targets) 

def test_return_state():
    input_size = 10
    timesteps = 6
    units = 2
    num_samples = 32

    for layer_class in [keras.layers.CuDNNGRU, keras.layers.CuDNNLSTM]:
        num_states = 2 if layer_class is keras.layers.CuDNNLSTM else 1

        inputs = keras.Input(batch_shape=(num_samples, timesteps, input_size))
        layer = layer_class(units, return_state=True, stateful=True)
        outputs = layer(inputs)
        output, state = outputs[0], outputs[1:]
        assert len(state) == num_states
        model = keras.models.Model(inputs, state[0])

        inputs = np.random.random((num_samples, timesteps, input_size))
        state = model.predict(inputs)
        np.testing.assert_allclose(
            keras.backend.eval(layer.states[0]), state, atol=1e-4) 

def test_specify_initial_state_keras_tensor():
    input_size = 10
    timesteps = 6
    units = 2
    num_samples = 32
    for layer_class in [keras.layers.CuDNNGRU, keras.layers.CuDNNLSTM]:
        num_states = 2 if layer_class is keras.layers.CuDNNLSTM else 1

        inputs = keras.Input((timesteps, input_size))
        initial_state = [keras.Input((units,)) for _ in range(num_states)]
        layer = layer_class(units)
        if len(initial_state) == 1:
            output = layer(inputs, initial_state=initial_state[0])
        else:
            output = layer(inputs, initial_state=initial_state)
        assert initial_state[0] in layer._inbound_nodes[0].input_tensors

        model = keras.models.Model([inputs] + initial_state, output)
        model.compile(loss='categorical_crossentropy', optimizer='adam')

        inputs = np.random.random((num_samples, timesteps, input_size))
        initial_state = [np.random.random((num_samples, units))
                         for _ in range(num_states)]
        targets = np.random.random((num_samples, units))
        model.fit([inputs] + initial_state, targets) 

def test_return_state():
    input_size = 10
    timesteps = 6
    units = 2
    num_samples = 32

    for layer_class in [keras.layers.CuDNNGRU, keras.layers.CuDNNLSTM]:
        num_states = 2 if layer_class is keras.layers.CuDNNLSTM else 1

        inputs = keras.Input(batch_shape=(num_samples, timesteps, input_size))
        layer = layer_class(units, return_state=True, stateful=True)
        outputs = layer(inputs)
        output, state = outputs[0], outputs[1:]
        assert len(state) == num_states
        model = keras.models.Model(inputs, state[0])

        inputs = np.random.random((num_samples, timesteps, input_size))
        state = model.predict(inputs)
        np.testing.assert_allclose(
            keras.backend.eval(layer.states[0]), state, atol=1e-4) 

def test_specify_initial_state_keras_tensor():
    input_size = 10
    timesteps = 6
    units = 2
    num_samples = 32
    for layer_class in [keras.layers.CuDNNGRU, keras.layers.CuDNNLSTM]:
        num_states = 2 if layer_class is keras.layers.CuDNNLSTM else 1

        inputs = keras.Input((timesteps, input_size))
        initial_state = [keras.Input((units,)) for _ in range(num_states)]
        layer = layer_class(units)
        if len(initial_state) == 1:
            output = layer(inputs, initial_state=initial_state[0])
        else:
            output = layer(inputs, initial_state=initial_state)
        assert initial_state[0] in layer._inbound_nodes[0].input_tensors

        model = keras.models.Model([inputs] + initial_state, output)
        model.compile(loss='categorical_crossentropy', optimizer='adam')

        inputs = np.random.random((num_samples, timesteps, input_size))
        initial_state = [np.random.random((num_samples, units))
                         for _ in range(num_states)]
        targets = np.random.random((num_samples, units))
        model.fit([inputs] + initial_state, targets) 

def test_specify_initial_state_keras_tensor():
    input_size = 10
    timesteps = 6
    units = 2
    num_samples = 32
    for layer_class in [keras.layers.CuDNNGRU, keras.layers.CuDNNLSTM]:
        num_states = 2 if layer_class is keras.layers.CuDNNLSTM else 1

        inputs = keras.Input((timesteps, input_size))
        initial_state = [keras.Input((units,)) for _ in range(num_states)]
        layer = layer_class(units)
        if len(initial_state) == 1:
            output = layer(inputs, initial_state=initial_state[0])
        else:
            output = layer(inputs, initial_state=initial_state)
        assert initial_state[0] in layer._inbound_nodes[0].input_tensors

        model = keras.models.Model([inputs] + initial_state, output)
        model.compile(loss='categorical_crossentropy', optimizer='adam')

        inputs = np.random.random((num_samples, timesteps, input_size))
        initial_state = [np.random.random((num_samples, units))
                         for _ in range(num_states)]
        targets = np.random.random((num_samples, units))
        model.fit([inputs] + initial_state, targets) 

def _save(model, base_model, layers, labels, random_seed, checkpoints_dir):
    from keras.layers import Flatten, Dense
    from keras import Model
    nclasses = len(labels)
    x = Flatten()(base_model.output)
    x = _makenet(x, layers, dropout=None, random_seed=random_seed)
    predictions = Dense(nclasses, activation="softmax", name="predictions")(x)
    model_final = Model(inputs=base_model.input, outputs=predictions)

    for i in range(layers - 1):
        weights = model.get_layer(name='dense_layer_{}'.format(i)).get_weights()
        model_final.get_layer(name='dense_layer_{}'.format(i)).set_weights(weights)

    weights = model.get_layer(name='predictions').get_weights()
    model_final.get_layer(name='predictions').set_weights(weights)

    model_final.save(os.path.join(checkpoints_dir, "model.h5"))
    with open(os.path.join(checkpoints_dir, "labels.txt"), "w") as f:
        f.write("\n".join(labels))
    return model_final 

def model(self, activation='softmax'):
        self.learning_to_downsample()
        self.global_feature_extractor()
        self.feature_fusion()
        self.classifier()
        self.output_layer = self.activation(activation)

        model = keras.Model(inputs=self.input_layer,
                            outputs=self.output_layer,
                            name='Fast_SCNN')
        return model 

def load_model(weights_path: str, base_model: keras.Model, tasks_meta_data: List[TaskMetadata]):
    model = train_model(base_model, is_causal=False, tasks_meta_data=tasks_meta_data, pretrain_generator=None,
                        finetune_generator=None)
    model.load_weights(weights_path)
    return model