自学教程：Python layers.RepeatVector方法代码示例

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import RepeatVector [as 别名]def test_repeat_vector(self):        from keras.layers import RepeatVector        model = Sequential()        model.add(RepeatVector(3, input_shape=(5,)))        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))        six.assertCountEqual(            self, input_names, [x.name for x in spec.description.input]        )        self.assertEquals(len(spec.description.output), len(output_names))        six.assertCountEqual(            self, output_names, [x.name for x in spec.description.output]        )        layers = spec.neuralNetwork.layers        self.assertIsNotNone(layers[0].sequenceRepeat) 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import RepeatVector [as 别名]def test_repeat_vector(self):        from keras.layers import RepeatVector        model = Sequential()        model.add(RepeatVector(3, input_shape=(5,)))        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))        self.assertEqual(            sorted(input_names), sorted(map(lambda x: x.name, spec.description.input))        )        self.assertEquals(len(spec.description.output), len(output_names))        self.assertEqual(            sorted(output_names), sorted(map(lambda x: x.name, spec.description.output))        )        layers = spec.neuralNetwork.layers        self.assertIsNotNone(layers[0].sequenceRepeat) 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import RepeatVector [as 别名]def repeat_vector(inputs):    """    Temporary solution:    Use this function within a Lambda layer to get a repeated layer with a variable 1-st dimension (seq_len).    May be useful to further feed it to a Concatenate layer.    inputs == (layer_for_repeat, layer_for_getting_rep_num):        layer_for_repeat:           shape == (batch_size, vector_dim)        layer_for_getting_rep_num:  shape == (batch_size, seq_len, ...)    :return:        repeated layer_for_repeat, shape == (batch_size, seq_len, vector_dim)    """    layer_for_repeat, layer_for_getting_rep_num = inputs    repeated_vector = RepeatVector(        n=K.shape(layer_for_getting_rep_num)[1], name='custom_repeat_vector')(layer_for_repeat)    # shape == (batch_size, seq_len, vector_dim)    return repeated_vector 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import RepeatVector [as 别名]def AlternativeRNNModel(vocab_size, max_len, rnnConfig, model_type):	embedding_size = rnnConfig['embedding_size']	if model_type == 'inceptionv3':		# InceptionV3 outputs a 2048 dimensional vector for each image, which we'll feed to RNN Model		image_input = Input(shape=(2048,))	elif model_type == 'vgg16':		# VGG16 outputs a 4096 dimensional vector for each image, which we'll feed to RNN Model		image_input = Input(shape=(4096,))	image_model_1 = Dense(embedding_size, activation='relu')(image_input)	image_model = RepeatVector(max_len)(image_model_1)	caption_input = Input(shape=(max_len,))	# mask_zero: We zero pad inputs to the same length, the zero mask ignores those inputs. E.g. it is an efficiency.	caption_model_1 = Embedding(vocab_size, embedding_size, mask_zero=True)(caption_input)	# Since we are going to predict the next word using the previous words	# (length of previous words changes with every iteration over the caption), we have to set return_sequences = True.	caption_model_2 = LSTM(rnnConfig['LSTM_units'], return_sequences=True)(caption_model_1)	# caption_model = TimeDistributed(Dense(embedding_size, activation='relu'))(caption_model_2)	caption_model = TimeDistributed(Dense(embedding_size))(caption_model_2)	# Merging the models and creating a softmax classifier	final_model_1 = concatenate([image_model, caption_model])	# final_model_2 = LSTM(rnnConfig['LSTM_units'], return_sequences=False)(final_model_1)	final_model_2 = Bidirectional(LSTM(rnnConfig['LSTM_units'], return_sequences=False))(final_model_1)	# final_model_3 = Dense(rnnConfig['dense_units'], activation='relu')(final_model_2)	# final_model = Dense(vocab_size, activation='softmax')(final_model_3)	final_model = Dense(vocab_size, activation='softmax')(final_model_2)	model = Model(inputs=[image_input, caption_input], outputs=final_model)	model.compile(loss='categorical_crossentropy', optimizer='adam')	# model.compile(loss='categorical_crossentropy', optimizer='rmsprop')	return model 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import RepeatVector [as 别名]def create_model(self, ret_model = False):        #base_model = VGG16(weights='imagenet', include_top=False, input_shape = (224, 224, 3))        #base_model.trainable=False        image_model = Sequential()        #image_model.add(base_model)        #image_model.add(Flatten())        image_model.add(Dense(EMBEDDING_DIM, input_dim = 4096, activation='relu'))        image_model.add(RepeatVector(self.max_cap_len))        lang_model = Sequential()        lang_model.add(Embedding(self.vocab_size, 256, input_length=self.max_cap_len))        lang_model.add(LSTM(256,return_sequences=True))        lang_model.add(TimeDistributed(Dense(EMBEDDING_DIM)))        model = Sequential()        model.add(Merge([image_model, lang_model], mode='concat'))        model.add(LSTM(1000,return_sequences=False))        model.add(Dense(self.vocab_size))        model.add(Activation('softmax'))        print "Model created!"        if(ret_model==True):            return model        model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy'])        return model 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import RepeatVector [as 别名]def _test_one_to_many(self, keras_major_version):        params = (            dict(                input_dims=[1, 10],                activation="tanh",                return_sequences=False,                output_dim=3,            ),        )        number_of_times = 4        model = Sequential()        model.add(RepeatVector(number_of_times, input_shape=(10,)))        if keras_major_version == 2:            model.add(                LSTM(                    params[0]["output_dim"],                    input_shape=params[0]["input_dims"],                    activation=params[0]["activation"],                    recurrent_activation="sigmoid",                    return_sequences=True,                )            )        else:            model.add(                LSTM(                    output_dim=params[0]["output_dim"],                    activation=params[0]["activation"],                    inner_activation="sigmoid",                    return_sequences=True,                )            )        relative_error, keras_preds, coreml_preds = simple_model_eval(params, model)        # print relative_error, '/n', keras_preds, '/n', coreml_preds, '/n'        for i in range(len(relative_error)):            self.assertLessEqual(relative_error[i], 0.01) 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import RepeatVector [as 别名]def base_model(feature_len=1, after_day=1, input_shape=(20, 1)):    model = Sequential()    model.add(LSTM(units=100, return_sequences=False, input_shape=input_shape))    #model.add(LSTM(units=100, return_sequences=False, input_shape=input_shape))    # one to many    model.add(RepeatVector(after_day))    model.add(LSTM(200, return_sequences=True))    #model.add(LSTM(50, return_sequences=True))    model.add(TimeDistributed(Dense(units=feature_len, activation='linear')))    return model 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import RepeatVector [as 别名]def seq2seq_attention(feature_len=1, after_day=1, input_shape=(20, 1), time_step=20):    # Define the inputs of your model with a shape (Tx, feature)    X = Input(shape=input_shape)    # Initialize empty list of outputs    all_outputs = []    # Encoder: pre-attention LSTM    encoder = LSTM(units=100, return_state=True, return_sequences=True, name='encoder')    # Decoder: post-attention LSTM    decoder = LSTM(units=100, return_state=True, name='decoder')    # Output    decoder_output = Dense(units=feature_len, activation='linear', name='output')    model_output = Reshape((1, feature_len))    # Attention    repeator = RepeatVector(time_step)    concatenator = Concatenate(axis=-1)    densor = Dense(1, activation = "relu")    activator = Activation(softmax, name='attention_weights')    dotor =  Dot(axes = 1)    encoder_outputs, s, c = encoder(X)    for t in range(after_day):        context = one_step_attention(encoder_outputs, s, repeator, concatenator, densor, activator, dotor)        a, s, c = decoder(context, initial_state=[s, c])        outputs = decoder_output(a)        outputs = model_output(outputs)        all_outputs.append(outputs)    all_outputs = Lambda(lambda x: K.concatenate(x, axis=1))(all_outputs)    model = Model(inputs=X, outputs=all_outputs)    return model 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import RepeatVector [as 别名]def seq2seq_attention(feature_len=1, after_day=1, input_shape=(20, 1), time_step=20):    # Define the inputs of your model with a shape (Tx, feature)    X = Input(shape=input_shape)    s0 = Input(shape=(100, ), name='s0')    c0 = Input(shape=(100, ), name='c0')    s = s0    c = c0    # Initialize empty list of outputs    all_outputs = []    # Encoder: pre-attention LSTM    encoder = LSTM(units=100, return_state=False, return_sequences=True, name='encoder')    # Decoder: post-attention LSTM    decoder = LSTM(units=100, return_state=True, name='decoder')    # Output    decoder_output = Dense(units=feature_len, activation='linear', name='output')    model_output = Reshape((1, feature_len))    # Attention    repeator = RepeatVector(time_step)    concatenator = Concatenate(axis=-1)    densor = Dense(1, activation = "relu")    activator = Activation(softmax, name='attention_weights')    dotor =  Dot(axes = 1)    encoder_outputs = encoder(X)    for t in range(after_day):        context = one_step_attention(encoder_outputs, s, repeator, concatenator, densor, activator, dotor)        a, s, c = decoder(context, initial_state=[s, c])        outputs = decoder_output(a)        outputs = model_output(outputs)        all_outputs.append(outputs)    all_outputs = Lambda(lambda x: K.concatenate(x, axis=1))(all_outputs)    model = Model(inputs=[X, s0, c0], outputs=all_outputs)    return model 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import RepeatVector [as 别名]def call(self, inputs, mask=None):        # Import (symbolic) dimensions        max_atoms = K.shape(inputs)[1]        # By [farizrahman4u](https://github.com/fchollet/keras/issues/3995)        ones = layers.Lambda(lambda x: (x * 0 + 1)[:, 0, :], output_shape=lambda s: (s[0], s[2]))(inputs)        dropped = self.dropout_layer(ones)        dropped = layers.RepeatVector(max_atoms)(dropped)        return layers.Lambda(lambda x: x[0] * x[1], output_shape=lambda s: s[0])([inputs, dropped]) 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import RepeatVector [as 别名]def test_repeat_vector():    layer_test(layers.RepeatVector,               kwargs={'n': 3},               input_shape=(3, 2)) 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import RepeatVector [as 别名]def test_sequential_model_saving():    model = Sequential()    model.add(Dense(2, input_shape=(3,)))    model.add(RepeatVector(3))    model.add(TimeDistributed(Dense(3)))    model.compile(loss=losses.MSE,                  optimizer=optimizers.RMSprop(lr=0.0001),                  metrics=[metrics.categorical_accuracy],                  sample_weight_mode='temporal')    x = np.random.random((1, 3))    y = np.random.random((1, 3, 3))    model.train_on_batch(x, y)    out = model.predict(x)    _, fname = tempfile.mkstemp('.h5')    save_model(model, fname)    new_model = load_model(fname)    os.remove(fname)    out2 = new_model.predict(x)    assert_allclose(out, out2, atol=1e-05)    # test that new updates are the same with both models    x = np.random.random((1, 3))    y = np.random.random((1, 3, 3))    model.train_on_batch(x, y)    new_model.train_on_batch(x, y)    out = model.predict(x)    out2 = new_model.predict(x)    assert_allclose(out, out2, atol=1e-05) 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import RepeatVector [as 别名]def attention(inputs, single_attention_vector=False):    # attention机制    time_steps = k_keras.int_shape(inputs)[1]    input_dim = k_keras.int_shape(inputs)[2]    x = Permute((2, 1))(inputs)    x = Dense(time_steps, activation='softmax')(x)    if single_attention_vector:        x = Lambda(lambda x: k_keras.mean(x, axis=1))(x)        x = RepeatVector(input_dim)(x)    a_probs = Permute((2, 1))(x)    output_attention_mul = Multiply()([inputs, a_probs])    return output_attention_mul 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import RepeatVector [as 别名]def stateless_attention_model(**kwargs):    X = LSTM(kwargs['hidden_units'], kernel_initializer='he_normal', activation='tanh',             dropout=kwargs['dropout'], return_sequences=True)(kwargs['embeddings'])    attention_layer = Permute((2, 1))(X)    attention_layer = Dense(kwargs['max_tweet_length'], activation='softmax')(attention_layer)    attention_layer = Lambda(lambda x: K.mean(x, axis=1), name='dim_reduction')(attention_layer)    attention_layer = RepeatVector(int(X.shape[2]))(attention_layer)    attention_probabilities = Permute((2, 1), name='attention_probs')(attention_layer)    attention_layer = Multiply()([X, attention_probabilities])    attention_layer = Flatten()(attention_layer)    return attention_layer