Python keras.Model() Examples
The following are 30 code examples for showing how to use keras.Model(). These examples are extracted from open source projects. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example.
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Example 1
Project: vergeml Author: mme File: features.py License: MIT License | 6 votes |
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
Example 2
Project: BERT Author: yyht File: model.py License: Apache License 2.0 | 6 votes |
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
Example 3
Project: BERT Author: yyht File: model.py License: Apache License 2.0 | 6 votes |
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')
Example 4
Project: BERT-keras Author: Separius File: model.py License: GNU General Public License v3.0 | 6 votes |
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')
Example 5
Project: BERT-keras Author: Separius File: load.py License: GNU General Public License v3.0 | 6 votes |
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
Example 6
Project: dts Author: albertogaspar File: FFNN.py License: MIT License | 6 votes |
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
Example 7
Project: dts Author: albertogaspar File: FFNN.py License: MIT License | 6 votes |
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
Example 8
Project: dts Author: albertogaspar File: Recurrent.py License: MIT License | 6 votes |
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
Example 9
Project: FractalAI Author: Guillemdb File: dnn_train.py License: GNU Affero General Public License v3.0 | 6 votes |
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
Example 10
Project: keras-transformer Author: kpot File: utils.py License: MIT License | 6 votes |
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.')
Example 11
Project: keras-pandas Author: bjherger File: testCategorical.py License: MIT License | 6 votes |
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())
Example 12
Project: keras-pandas Author: bjherger File: testText.py License: MIT License | 6 votes |
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
Example 13
Project: keras-pandas Author: bjherger File: testNumerical.py License: MIT License | 6 votes |
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())
Example 14
Project: bi-lstm-crf Author: GlassyWing File: core.py License: Apache License 2.0 | 6 votes |
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
Example 15
Project: DeepLearning_Wavelet-LSTM Author: hello-sea File: cudnn_recurrent_test.py License: MIT License | 6 votes |
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)
Example 16
Project: DeepLearning_Wavelet-LSTM Author: hello-sea File: cudnn_recurrent_test.py License: MIT License | 6 votes |
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)
Example 17
Project: DeepLearning_Wavelet-LSTM Author: hello-sea File: cudnn_recurrent_test.py License: MIT License | 6 votes |
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)
Example 18
Project: DeepLearning_Wavelet-LSTM Author: hello-sea File: cudnn_recurrent_test.py License: MIT License | 6 votes |
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)
Example 19
Project: DeepLearning_Wavelet-LSTM Author: hello-sea File: cudnn_recurrent_test.py License: MIT License | 6 votes |
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)
Example 20
Project: DeepLearning_Wavelet-LSTM Author: hello-sea File: cudnn_recurrent_test.py License: MIT License | 6 votes |
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)
Example 21
Project: DeepLearning_Wavelet-LSTM Author: hello-sea File: cudnn_recurrent_test.py License: MIT License | 6 votes |
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)
Example 22
Project: DeepLearning_Wavelet-LSTM Author: hello-sea File: cudnn_recurrent_test.py License: MIT License | 6 votes |
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)
Example 23
Project: DeepLearning_Wavelet-LSTM Author: hello-sea File: cudnn_recurrent_test.py License: MIT License | 6 votes |
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)
Example 24
Project: DeepLearning_Wavelet-LSTM Author: hello-sea File: cudnn_recurrent_test.py License: MIT License | 6 votes |
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)
Example 25
Project: DeepLearning_Wavelet-LSTM Author: hello-sea File: cudnn_recurrent_test.py License: MIT License | 6 votes |
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)
Example 26
Project: DeepLearning_Wavelet-LSTM Author: hello-sea File: cudnn_recurrent_test.py License: MIT License | 6 votes |
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)
Example 27
Project: DeepLearning_Wavelet-LSTM Author: hello-sea File: cudnn_recurrent_test.py License: MIT License | 6 votes |
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)
Example 28
Project: vergeml Author: mme File: imagenet.py License: MIT License | 5 votes |
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
Example 29
Project: Face-skin-hair-segmentaiton-and-skin-color-evaluation Author: JACKYLUO1991 File: fast_scnn.py License: Apache License 2.0 | 5 votes |
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
Example 30
Project: BERT Author: yyht File: train.py License: Apache License 2.0 | 5 votes |
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