这篇教程Python layers.dot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.dot方法的典型用法代码示例。如果您正苦于以下问题：Python layers.dot方法的具体用法？Python layers.dot怎么用？Python layers.dot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.dot方法的8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: self_attention# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import dot [as 别名]def self_attention(x): ''' . stands for dot product * stands for elemwise multiplication m = x . transpose(x) n = softmax(m) o = n . x a = o * x return a ''' m = dot([x, x], axes=[2,2]) n = Activation('softmax')(m) o = dot([n, x], axes=[2,1]) a = multiply([o, x]) return a
示例2: test_tiny_cos_random# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import dot [as 别名]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_model(model)
示例3: create_model# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import dot [as 别名]def create_model(self): user_id_input = Input(shape=[1], name='user') item_id_input = Input(shape=[1], name='item') user_embedding = Embedding(output_dim=EMBEDDING_SIZE, input_dim=self.max_user_id + 1, input_length=1, name='user_embedding')(user_id_input) item_embedding = Embedding(output_dim=EMBEDDING_SIZE, input_dim=self.max_item_id + 1, input_length=1, name='item_embedding')(item_id_input) # reshape from shape: (batch_size, input_length, embedding_size) # to shape: (batch_size, input_length * embedding_size) which is # equal to shape: (batch_size, embedding_size) user_vecs = Flatten()(user_embedding) item_vecs = Flatten()(item_embedding) # y = merge([user_vecs, item_vecs], mode='dot', output_shape=(1,)) y = dot([user_vecs, item_vecs], axes=1) model = Model(inputs=[user_id_input, item_id_input], outputs=[y]) model.compile(optimizer='adam', loss='mae') return model
开发者ID:chen0040，项目名称:keras-recommender，代码行数:24，代码来源:cf.py
示例4: test_merge_dot# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import dot [as 别名]def test_merge_dot(): i1 = layers.Input(shape=(4,)) i2 = layers.Input(shape=(4,)) o = layers.dot([i1, i2], axes=1) assert o._keras_shape == (None, 1) model = models.Model([i1, i2], o) dot_layer = layers.Dot(axes=1) o2 = dot_layer([i1, i2]) assert dot_layer.output_shape == (None, 1) x1 = np.random.random((2, 4)) x2 = np.random.random((2, 4)) out = model.predict([x1, x2]) assert out.shape == (2, 1) expected = np.zeros((2, 1)) expected[0, 0] = np.dot(x1[0], x2[0]) expected[1, 0] = np.dot(x1[1], x2[1]) assert_allclose(out, expected, atol=1e-4) # Test with negative tuple of axes. o = layers.dot([i1, i2], axes=(-1, -1)) assert o._keras_shape == (None, 1) model = models.Model([i1, i2], o) out = model.predict([x1, x2]) assert out.shape == (2, 1) assert_allclose(out, expected, atol=1e-4)
开发者ID:hello-sea，项目名称:DeepLearning_Wavelet-LSTM，代码行数:29，代码来源:merge_test.py
示例5: create_model# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import dot [as 别名]def create_model(self): dat_input = Input(shape=(self.tdatlen,)) com_input = Input(shape=(self.comlen,)) sml_input = Input(shape=(self.smllen,)) ee = Embedding(output_dim=self.embdims, input_dim=self.tdatvocabsize, mask_zero=False)(dat_input) se = Embedding(output_dim=self.smldims, input_dim=self.smlvocabsize, mask_zero=False)(sml_input) se_enc = CuDNNGRU(self.recdims, return_state=True, return_sequences=True) seout, state_sml = se_enc(se) enc = CuDNNGRU(self.recdims, return_state=True, return_sequences=True) encout, state_h = enc(ee, initial_state=state_sml) de = Embedding(output_dim=self.embdims, input_dim=self.comvocabsize, mask_zero=False)(com_input) dec = CuDNNGRU(self.recdims, return_sequences=True) decout = dec(de, initial_state=state_h) attn = dot([decout, encout], axes=[2, 2]) attn = Activation('softmax')(attn) context = dot([attn, encout], axes=[2, 1]) ast_attn = dot([decout, seout], axes=[2, 2]) ast_attn = Activation('softmax')(ast_attn) ast_context = dot([ast_attn, seout], axes=[2, 1]) context = concatenate([context, decout, ast_context]) out = TimeDistributed(Dense(self.recdims, activation="relu"))(context) out = Flatten()(out) out = Dense(self.comvocabsize, activation="softmax")(out) model = Model(inputs=[dat_input, com_input, sml_input], outputs=out) if self.config['multigpu']: model = keras.utils.multi_gpu_model(model, gpus=2) model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']) return self.config, model
示例6: bi_modal_attention# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import dot [as 别名]def bi_modal_attention(x, y): ''' . stands for dot product * stands for elemwise multiplication {} stands for concatenation m1 = x . transpose(y) || m2 = y . transpose(x) n1 = softmax(m1) || n2 = softmax(m2) o1 = n1 . y || o2 = m2 . x a1 = o1 * x || a2 = o2 * y return {a1, a2} ''' m1 = dot([x, y], axes=[2, 2]) n1 = Activation('softmax')(m1) o1 = dot([n1, y], axes=[2, 1]) a1 = multiply([o1, x]) m2 = dot([y, x], axes=[2, 2]) n2 = Activation('softmax')(m2) o2 = dot([n2, x], axes=[2, 1]) a2 = multiply([o2, y]) return concatenate([a1, a2])
示例7: eltwise# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import dot [as 别名]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
示例8: skipgram_model# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import dot [as 别名]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
|
