我们从Python开源项目中，提取了以下7个代码示例，用于说明如何使用keras.layers.dot()。
def _get_model2(self): x1 = Input(shape=(10,)) x2 = Input(shape=(10,)) y = dot([ Dense(10)(x1), Dense(10)(x2) ], axes=1) model = Model(inputs=[x1, x2], outputs=y) model.compile(loss="mse", optimizer="adam") wrapped = OracleWrapper(model, BiasedReweightingPolicy(), score="loss") x = [np.random.rand(16, 10), np.random.rand(16, 10)] y = np.random.rand(16, 1) return model, wrapped, x, y
def test_tiny_cos_random(self): np.random.seed(1988) input_dim = 10 num_channels = 6 # Define a model input_tensor = Input(shape = (input_dim, )) x1 = Dense(num_channels)(input_tensor) x2 = Dense(num_channels)(x1) x3 = Dense(num_channels)(x1) x4 = dot([x2, x3], axes=-1, normalize=True) x5 = Dense(num_channels)(x4) model = Model(inputs=[input_tensor], outputs=[x5]) # Set some random weights model.set_weights([np.random.rand(*w.shape) for w in model.get_weights()]) # Get the coreml model self._test_keras_model(model)
def getModel(input_shape): Qfunc = Sequential() action = Input(shape=(Player.NUM_ACTIONS,)) screen = Input(shape=input_shape) Qfunc.add(Conv2D(32, (8, 8), strides=(4, 4), activation='relu', padding="same", input_shape=input_shape, data_format="channels_first")) Qfunc.add(Conv2D(64, (4, 4), strides=(2, 2), activation='relu', padding="same", data_format="channels_first")) Qfunc.add(Conv2D(64, (4, 4), activation='relu', padding="same", data_format="channels_first")) Qfunc.add(MaxPooling2D(pool_size=(2, 2))) Qfunc.add(Flatten()) Qfunc.add(Dense(128, activation='relu')) Qfunc.add(Dense(128, activation='relu')) Qfunc.add(Dense(Player.NUM_ACTIONS)) reward = Qfunc(screen) model = Model(inputs=[screen, action], outputs=dot([reward, action], axes=[1, 1])) return Qfunc, model
def __init__(self, *args, **kwargs): self.model = Sequential([ Dense(10, activation="relu", input_shape=(2,)), Dense(10, activation="relu"), Dense(2) ]) self.model.compile("sgd", "mse", metrics=["mae"]) x1 = Input(shape=(10,)) x2 = Input(shape=(10,)) y = dot([ Dense(10)(x1), Dense(10)(x2) ], axes=1) self.model2 = Model(inputs=[x1, x2], outputs=y) self.model2.compile(loss="mse", optimizer="adam") super(TestTraining, self).__init__(*args, **kwargs)
def eltwise(layer, layer_in, layerId): out = {} if (layer['params']['layer_type'] == 'Multiply'): # This input reverse is to handle visualization out[layerId] = multiply(layer_in[::-1]) elif (layer['params']['layer_type'] == 'Sum'): out[layerId] = add(layer_in[::-1]) elif (layer['params']['layer_type'] == 'Average'): out[layerId] = average(layer_in[::-1]) elif (layer['params']['layer_type'] == 'Dot'): out[layerId] = dot(layer_in[::-1], -1) else: out[layerId] = maximum(layer_in[::-1]) return out
def skipgram_model(vocab_size, embedding_dim=100, paradigm='Functional'): # Sequential paradigm if paradigm == 'Sequential': target = Sequential() target.add(Embedding(vocab_size, embedding_dim, input_length=1)) context = Sequential() context.add(Embedding(vocab_size, embedding_dim, input_length=1)) # merge the pivot and context models model = Sequential() model.add(Merge([target, context], mode='dot')) model.add(Reshape((1,), input_shape=(1,1))) model.add(Activation('sigmoid')) model.compile(optimizer='adam', loss='binary_crossentropy') return model # Functional paradigm elif paradigm == 'Functional': target = Input(shape=(1,), name='target') context = Input(shape=(1,), name='context') #print target.shape, context.shape shared_embedding = Embedding(vocab_size, embedding_dim, input_length=1, name='shared_embedding') embedding_target = shared_embedding(target) embedding_context = shared_embedding(context) #print embedding_target.shape, embedding_context.shape merged_vector = dot([embedding_target, embedding_context], axes=-1) reshaped_vector = Reshape((1,), input_shape=(1,1))(merged_vector) #print merged_vector.shape prediction = Dense(1, input_shape=(1,), activation='sigmoid')(reshaped_vector) #print prediction.shape model = Model(inputs=[target, context], outputs=prediction) model.compile(optimizer='adam', loss='binary_crossentropy') return model else: print('paradigm error') return None
def create_model(data): ''' Load keras model. ''' # Entity branch entity_inputs = Input(shape=(data[0].shape[1],)) entity_x = Dense(data[0].shape[1], activation='relu', kernel_constraint=maxnorm(3))(entity_inputs) entity_x = Dropout(0.25)(entity_x) #entity_x = Dense(50, activation='relu', # kernel_constraint=maxnorm(3))(entity_x) #entity_x = Dropout(0.25)(entity_x) # Candidate branch candidate_inputs = Input(shape=(data[1].shape[1],)) candidate_x = Dense(data[1].shape[1], activation='relu', kernel_constraint=maxnorm(3))(candidate_inputs) candidate_x = Dropout(0.25)(candidate_x) #candidate_x = Dense(50, activation='relu', # kernel_constraint=maxnorm(3))(candidate_x) #candidate_x = Dropout(0.25)(candidate_x) # Cosine proximity # cos_x = dot([entity_x, candidate_x], axes=1, normalize=False) # cos_x = concatenate([entity_x, candidate_x]) # cos_output = Dense(1, activation='sigmoid')(cos_x) # Match branch match_inputs = Input(shape=(data[2].shape[1],)) match_x = Dense(data[1].shape[1], activation='relu', kernel_constraint=maxnorm(3))(match_inputs) match_x = Dropout(0.25)(match_x) # Merge x = concatenate([entity_x, candidate_x, match_x]) x = Dense(32, activation='relu', kernel_constraint=maxnorm(3))(x) x = Dropout(0.25)(x) x = Dense(16, activation='relu', kernel_constraint=maxnorm(3))(x) x = Dropout(0.25)(x) x = Dense(8, activation='relu', kernel_constraint=maxnorm(3))(x) x = Dropout(0.25)(x) predictions = Dense(1, activation='sigmoid')(x) model = Model(inputs=[entity_inputs, candidate_inputs, match_inputs], outputs=predictions) model.compile(optimizer='RMSprop', loss='binary_crossentropy', metrics=['accuracy']) return model
