这篇教程Python layers.SimpleRNN方法代码示例写得很实用，希望能帮到您。
本文整理汇总了Python中keras.layers.SimpleRNN方法的典型用法代码示例。如果您正苦于以下问题：Python layers.SimpleRNN方法的具体用法？Python layers.SimpleRNN怎么用？Python layers.SimpleRNN使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在模块keras.layers的用法示例。 在下文中一共展示了layers.SimpleRNN方法的13个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于我们的系统推荐出更棒的Python代码示例。 示例1: _build_model# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import SimpleRNN [as 别名]def _build_model(self, num_features, num_actions, max_history_len): """Build a keras model and return a compiled model. :param max_history_len: The maximum number of historical turns used to decide on next action""" from keras.layers import Activation, Masking, Dense, SimpleRNN from keras.models import Sequential n_hidden = 8 # size of hidden layer in RNN # Build Model batch_input_shape = (None, max_history_len, num_features) model = Sequential() model.add(Masking(-1, batch_input_shape=batch_input_shape)) model.add(SimpleRNN(n_hidden, batch_input_shape=batch_input_shape)) model.add(Dense(input_dim=n_hidden, output_dim=num_actions)) model.add(Activation('softmax')) model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']) logger.debug(model.summary()) return model
开发者ID:Rowl1ng，项目名称:rasa_wechat，代码行数:25，代码来源:policy.py
示例2: test_tiny_sequence_simple_rnn_random# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import SimpleRNN [as 别名]def test_tiny_sequence_simple_rnn_random(self): np.random.seed(1988) input_dim = 2 input_length = 4 num_channels = 3 # Define a model model = Sequential() model.add(SimpleRNN(num_channels, input_shape=(input_length, input_dim))) # Set some random weights model.set_weights( [np.random.rand(*w.shape) * 0.2 - 0.1 for w in model.get_weights()] ) # Test the keras model self._test_model(model)
示例3: test_tiny_seq2seq_rnn_random# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import SimpleRNN [as 别名]def test_tiny_seq2seq_rnn_random(self): np.random.seed(1988) input_dim = 2 input_length = 4 num_channels = 3 # Define a model model = Sequential() model.add( SimpleRNN( num_channels, input_shape=(input_length, input_dim), return_sequences=True, ) ) # Set some random weights model.set_weights( [np.random.rand(*w.shape) * 0.2 - 0.1 for w in model.get_weights()] ) # Test the keras model self._test_model(model)
示例4: test_rnn_seq# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import SimpleRNN [as 别名]def test_rnn_seq(self): np.random.seed(1988) input_dim = 11 input_length = 5 # Define a model model = Sequential() model.add( SimpleRNN(20, input_shape=(input_length, input_dim), return_sequences=False) ) # Set some random weights model.set_weights( [np.random.rand(*w.shape) * 0.2 - 0.1 for w in model.get_weights()] ) # Test the keras model self._test_model(model)
示例5: test_medium_no_sequence_simple_rnn_random# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import SimpleRNN [as 别名]def test_medium_no_sequence_simple_rnn_random(self): np.random.seed(1988) input_dim = 10 input_length = 1 num_channels = 10 # Define a model model = Sequential() model.add(SimpleRNN(num_channels, input_shape=(input_length, input_dim))) # Set some random weights model.set_weights( [np.random.rand(*w.shape) * 0.2 - 0.1 for w in model.get_weights()] ) # Test the keras model self._test_model(model)
示例6: test_merge_mask_3d# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import SimpleRNN [as 别名]def test_merge_mask_3d(): rand = lambda *shape: np.asarray(np.random.random(shape) > 0.5, dtype='int32') # embeddings input_a = layers.Input(shape=(3,), dtype='int32') input_b = layers.Input(shape=(3,), dtype='int32') embedding = layers.Embedding(3, 4, mask_zero=True) embedding_a = embedding(input_a) embedding_b = embedding(input_b) # rnn rnn = layers.SimpleRNN(3, return_sequences=True) rnn_a = rnn(embedding_a) rnn_b = rnn(embedding_b) # concatenation merged_concat = legacy_layers.merge([rnn_a, rnn_b], mode='concat', concat_axis=-1) model = models.Model([input_a, input_b], [merged_concat]) model.compile(loss='mse', optimizer='sgd') model.fit([rand(2, 3), rand(2, 3)], [rand(2, 3, 6)])
开发者ID:hello-sea，项目名称:DeepLearning_Wavelet-LSTM，代码行数:22，代码来源:layers_test.py
示例7: test_keras_import# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import SimpleRNN [as 别名]def test_keras_import(self): model = Sequential() model.add(LSTM(64, return_sequences=True, input_shape=(10, 64))) model.add(SimpleRNN(32, return_sequences=True)) model.add(GRU(10, kernel_regularizer=regularizers.l2(0.01), bias_regularizer=regularizers.l2(0.01), recurrent_regularizer=regularizers.l2(0.01), activity_regularizer=regularizers.l2(0.01), kernel_constraint='max_norm', bias_constraint='max_norm', recurrent_constraint='max_norm')) model.build() json_string = Model.to_json(model) with open(os.path.join(settings.BASE_DIR, 'media', 'test.json'), 'w') as out: json.dump(json.loads(json_string), out, indent=4) sample_file = open(os.path.join(settings.BASE_DIR, 'media', 'test.json'), 'r') response = self.client.post(reverse('keras-import'), {'file': sample_file}) response = json.loads(response.content) layerId = sorted(response['net'].keys()) self.assertEqual(response['result'], 'success') self.assertGreaterEqual(len(response['net'][layerId[1]]['params']), 7) self.assertGreaterEqual(len(response['net'][layerId[3]]['params']), 7) self.assertGreaterEqual(len(response['net'][layerId[6]]['params']), 7)# ********** Embedding Layers **********
示例8: test_simple_rnn# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import SimpleRNN [as 别名]def test_simple_rnn(self): """ Test the conversion of a simple RNN layer. """ from keras.layers import SimpleRNN # Create a simple Keras model model = Sequential() model.add(SimpleRNN(32, input_dim=32, input_length=10)) input_names = ["input"] output_names = ["output"] spec = keras.convert(model, input_names, output_names).get_spec() self.assertIsNotNone(spec) # Test the model class self.assertIsNotNone(spec.description) self.assertTrue(spec.HasField("neuralNetwork")) # Test the inputs and outputs self.assertEquals(len(spec.description.input), len(input_names) + 1) self.assertEquals(input_names[0], spec.description.input[0].name) self.assertEquals(32, spec.description.input[1].type.multiArrayType.shape[0]) self.assertEquals(len(spec.description.output), len(output_names) + 1) self.assertEquals(output_names[0], spec.description.output[0].name) self.assertEquals(32, spec.description.output[0].type.multiArrayType.shape[0]) self.assertEquals(32, spec.description.output[1].type.multiArrayType.shape[0]) # Test the layer parameters. layers = spec.neuralNetwork.layers layer_0 = layers[0] self.assertIsNotNone(layer_0.simpleRecurrent) self.assertEquals(len(layer_0.input), 2) self.assertEquals(len(layer_0.output), 2)
示例9: test_tiny_no_sequence_simple_rnn_random# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import SimpleRNN [as 别名]def test_tiny_no_sequence_simple_rnn_random(self): np.random.seed(1988) input_dim = 10 input_length = 1 num_channels = 1 # Define a model model = Sequential() model.add(SimpleRNN(num_channels, input_shape=(input_length, input_dim))) # Set some random weights model.set_weights([np.random.rand(*w.shape) for w in model.get_weights()]) # Test the keras model self._test_model(model)
示例10: test_lstm_td# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import SimpleRNN [as 别名]def test_lstm_td(self): np.random.seed(1988) input_dim = 2 input_length = 4 num_channels = 3 # Define a model model = Sequential() model.add( SimpleRNN( num_channels, return_sequences=True, input_shape=(input_length, input_dim), ) ) model.add(TimeDistributed(Dense(5))) # Set some random weights model.set_weights( [np.random.rand(*w.shape) * 0.2 - 0.1 for w in model.get_weights()] ) # Test the keras model self._test_model(model) # Making sure that giant channel sizes get handled correctly
示例11: test_simple_rnn# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import SimpleRNN [as 别名]def test_simple_rnn(self): """ Test the conversion of a simple RNN layer. """ from keras.layers import SimpleRNN # Create a simple Keras model model = Sequential() model.add(SimpleRNN(32, input_shape=(10, 32))) input_names = ["input"] output_names = ["output"] spec = keras.convert(model, input_names, output_names).get_spec() self.assertIsNotNone(spec) # Test the model class self.assertIsNotNone(spec.description) self.assertTrue(spec.HasField("neuralNetwork")) # Test the inputs and outputs self.assertEquals(len(spec.description.input), len(input_names) + 1) self.assertEquals(input_names[0], spec.description.input[0].name) self.assertEquals(32, spec.description.input[1].type.multiArrayType.shape[0]) self.assertEquals(len(spec.description.output), len(output_names) + 1) self.assertEquals(output_names[0], spec.description.output[0].name) self.assertEquals(32, spec.description.output[0].type.multiArrayType.shape[0]) self.assertEquals(32, spec.description.output[1].type.multiArrayType.shape[0]) # Test the layer parameters. layers = spec.neuralNetwork.layers layer_0 = layers[0] self.assertIsNotNone(layer_0.simpleRecurrent) self.assertEquals(len(layer_0.input), 2) self.assertEquals(len(layer_0.output), 2)
示例12: test_Bidirectional_trainable# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import SimpleRNN [as 别名]def test_Bidirectional_trainable(): # test layers that need learning_phase to be set x = Input(shape=(3, 2)) layer = wrappers.Bidirectional(layers.SimpleRNN(3)) _ = layer(x) assert len(layer.trainable_weights) == 6 layer.trainable = False assert len(layer.trainable_weights) == 0 layer.trainable = True assert len(layer.trainable_weights) == 6
示例13: test_temporal_classification_functional# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import SimpleRNN [as 别名]def test_temporal_classification_functional(): ''' Classify temporal sequences of float numbers of length 3 into 2 classes using single layer of GRU units and softmax applied to the last activations of the units ''' np.random.seed(1337) (x_train, y_train), (x_test, y_test) = get_test_data(num_train=200, num_test=20, input_shape=(3, 4), classification=True, num_classes=2) y_train = to_categorical(y_train) y_test = to_categorical(y_test) inputs = layers.Input(shape=(x_train.shape[1], x_train.shape[2])) x = layers.SimpleRNN(8)(inputs) outputs = layers.Dense(y_train.shape[-1], activation='softmax')(x) model = keras.models.Model(inputs, outputs) model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy']) history = model.fit(x_train, y_train, epochs=4, batch_size=10, validation_data=(x_test, y_test), verbose=0) assert(history.history['acc'][-1] >= 0.8)
|
