python类Conv1D()的实例源码

models.py 文件源码 项目:AutoSleepScorerDev 作者: skjerns 项目源码 文件源码 阅读 25 收藏 0 点赞 0 评论 0
def tsinalis(input_shape, n_classes):
    """
    Input size should be [batch, 1d, 2d, ch] = (None, 1, 15000, 1)
    """
    model = Sequential(name='Tsinalis')
    model.add(Conv1D (kernel_size = (200), filters = 20, input_shape=input_shape, activation='relu'))
    print(model.input_shape)
    print(model.output_shape)
    model.add(MaxPooling1D(pool_size = (20), strides=(10)))
    print(model.output_shape)
    model.add(keras.layers.core.Reshape([20,-1,1]))
    print(model.output_shape)    
    model.add(Conv2D (kernel_size = (20,30), filters = 400, activation='relu'))
    print(model.output_shape)
    model.add(MaxPooling2D(pool_size = (1,10), strides=(1,2)))
    print(model.output_shape)
    model.add(Flatten())
    print(model.output_shape)
    model.add(Dense (500, activation='relu'))
    model.add(Dense (500, activation='relu'))
    model.add(Dense(n_classes, activation = 'softmax',activity_regularizer=keras.regularizers.l2()  ))
    model.compile( loss='categorical_crossentropy', optimizer=keras.optimizers.SGD(), metrics=[keras.metrics.categorical_accuracy])
    return model
model.py 文件源码 项目:keras_detect_tool_wear 作者: kidozh 项目源码 文件源码 阅读 21 收藏 0 点赞 0 评论 0
def first_block(tensor_input,filters,kernel_size=3,pooling_size=1,dropout=0.5):
    k1,k2 = filters

    out = Conv1D(k1,1,padding='same')(tensor_input)
    out = BatchNormalization()(out)
    out = Activation('relu')(out)
    out = Dropout(dropout)(out)
    out = Conv1D(k2,kernel_size,padding='same')(out)


    pooling = MaxPooling1D(pooling_size,padding='same')(tensor_input)


    # out = merge([out,pooling],mode='sum')
    out = add([out,pooling])
    return out
test_views.py 文件源码 项目:Fabrik 作者: Cloud-CV 项目源码 文件源码 阅读 22 收藏 0 点赞 0 评论 0
def test_keras_import(self):
        # Pad 1D
        model = Sequential()
        model.add(ZeroPadding1D(2, input_shape=(224, 3)))
        model.add(Conv1D(32, 7, strides=2))
        model.build()
        self.pad_test(model, 'pad_w', 2)
        # Pad 2D
        model = Sequential()
        model.add(ZeroPadding2D(2, input_shape=(224, 224, 3)))
        model.add(Conv2D(32, 7, strides=2))
        model.build()
        self.pad_test(model, 'pad_w', 2)
        # Pad 3D
        model = Sequential()
        model.add(ZeroPadding3D(2, input_shape=(224, 224, 224, 3)))
        model.add(Conv3D(32, 7, strides=2))
        model.build()
        self.pad_test(model, 'pad_w', 2)


# ********** Export json tests **********

# ********** Data Layers Test **********
models.py 文件源码 项目:AutoSleepScorerDev 作者: skjerns 项目源码 文件源码 阅读 17 收藏 0 点赞 0 评论 0
def rcnn(input_shape, n_classes):
    """
    Input size should be [batch, 1d, ch] = (XXX, 3000, 1)
    """
    model = Sequential(name='RCNN test')
    model.add(Conv1D (kernel_size = (200), filters = 20, batch_input_shape=input_shape, activation='elu'))
    model.add(MaxPooling1D(pool_size = (20), strides=(10)))
    model.add(Conv1D (kernel_size = (20), filters = 200, activation='elu'))
    model.add(MaxPooling1D(pool_size = (10), strides=(3)))
    model.add(Conv1D (kernel_size = (20), filters = 200, activation='elu'))
    model.add(MaxPooling1D(pool_size = (10), strides=(3)))
    model.add(Dense (512, activation='elu'))
    model.add(Dense (512, activation='elu'))
    model.add(Reshape((1,model.output_shape[1])))
    model.add(LSTM(256, stateful=True, return_sequences=False))
    model.add(Dropout(0.3))
    model.add(Dense(n_classes, activation = 'sigmoid'))
    model.compile(loss='categorical_crossentropy', optimizer=Adadelta())
    return model
model.py 文件源码 项目:keras_detect_tool_wear 作者: kidozh 项目源码 文件源码 阅读 23 收藏 0 点赞 0 评论 0
def repeated_block(x,filters,kernel_size=3,pooling_size=1,dropout=0.5):

    k1,k2 = filters


    out = BatchNormalization()(x)
    out = Activation('relu')(out)
    out = Conv1D(k1,kernel_size,strides=2,padding='same')(out)
    out = BatchNormalization()(out)
    out = Activation('relu')(out)
    out = Dropout(dropout)(out)
    out = Conv1D(k2,kernel_size,strides=2,padding='same')(out)


    pooling = MaxPooling1D(pooling_size,strides=4,padding='same')(x)

    out = add([out, pooling])

    #out = merge([out,pooling])
    return out
malwaresnet.py 文件源码 项目:youarespecial 作者: endgameinc 项目源码 文件源码 阅读 29 收藏 0 点赞 0 评论 0
def ResidualBlock1D_helper(layers, kernel_size, filters, final_stride=1):
    def f(_input):
        basic = _input
        for ln in range(layers):
            #basic = BatchNormalization()( basic ) # triggers known keras bug w/ TimeDistributed: https://github.com/fchollet/keras/issues/5221
            basic = ELU()(basic)  
            basic = Conv1D(filters, kernel_size, kernel_initializer='he_normal',
                           kernel_regularizer=l2(1.e-4), padding='same')(basic)

        # note that this strides without averaging
        return AveragePooling1D(pool_size=1, strides=final_stride)(Add()([_input, basic]))

    return f
test_surgeon.py 文件源码 项目:keras-surgeon 作者: BenWhetton 项目源码 文件源码 阅读 28 收藏 0 点赞 0 评论 0
def layer_test_helper_1d_global(layer, channel_index):
    # This should test that the output is the correct shape so it should pass
    # into a Dense layer rather than a Conv layer.
    # The weighted layer is the previous layer,
    # Create model
    main_input = Input(shape=list(random.randint(10, 20, size=2)))
    x = Conv1D(3, 3)(main_input)
    x = layer(x)
    main_output = Dense(5)(x)
    model = Model(inputs=main_input, outputs=main_output)

    # Delete channels
    del_layer_index = 1
    next_layer_index = 3
    del_layer = model.layers[del_layer_index]
    new_model = operations.delete_channels(model, del_layer, channel_index)
    new_w = new_model.layers[next_layer_index].get_weights()

    # Calculate next layer's correct weights
    channel_count = getattr(del_layer, utils.get_channels_attr(del_layer))
    channel_index = [i % channel_count for i in channel_index]
    correct_w = model.layers[next_layer_index].get_weights()
    correct_w[0] = np.delete(correct_w[0], channel_index, axis=0)

    assert weights_equal(correct_w, new_w)
test_keras2.py 文件源码 项目:coremltools 作者: apple 项目源码 文件源码 阅读 21 收藏 0 点赞 0 评论 0
def test_conv1d_lstm(self):
        from keras.layers import Conv1D, LSTM, Dense
        model = Sequential()
        # input_shape = (time_step, dimensions)
        model.add(Conv1D(32,3,padding='same',input_shape=(10,8)))
        # conv1d output shape = (None, 10, 32)
        model.add(LSTM(24))
        model.add(Dense(1, activation='sigmoid'))

        input_names = ['input']
        output_names = ['output']
        spec = keras.convert(model, input_names, output_names).get_spec()

        self.assertIsNotNone(spec)
        self.assertTrue(spec.HasField('neuralNetwork'))

        # Test the inputs and outputs
        self.assertEquals(len(spec.description.input), len(input_names) + 2)
        self.assertEquals(len(spec.description.output), len(output_names) + 2)

        # Test the layer parameters.
        layers = spec.neuralNetwork.layers
        self.assertIsNotNone(layers[0].convolution)
        self.assertIsNotNone(layers[1].simpleRecurrent)
        self.assertIsNotNone(layers[2].innerProduct)
test_keras2_numeric.py 文件源码 项目:coremltools 作者: apple 项目源码 文件源码 阅读 17 收藏 0 点赞 0 评论 0
def test_tiny_conv1d_dilated_random(self):
        np.random.seed(1988)
        input_shape = (20, 1)
        num_kernels = 2
        filter_length = 3

        # Define a model
        model = Sequential()
        model.add(Conv1D(num_kernels, kernel_size = filter_length, padding = 'valid',
            input_shape = input_shape, dilation_rate = 3))

        # Set some random weights
        model.set_weights([np.random.rand(*w.shape) for w in model.get_weights()])

        # Test the keras model
        self._test_keras_model(model)
test_keras2_numeric.py 文件源码 项目:coremltools 作者: apple 项目源码 文件源码 阅读 22 收藏 0 点赞 0 评论 0
def test_tiny_conv_upsample_1d_random(self):
        np.random.seed(1988)
        input_dim = 2
        input_length = 10
        filter_length = 3
        nb_filters = 4
        model = Sequential()
        model.add(Conv1D(nb_filters, kernel_size = filter_length, padding='same',
            input_shape=(input_length, input_dim)))
        model.add(UpSampling1D(size = 2))

        # Set some random weights
        model.set_weights([np.random.rand(*w.shape) for w in model.get_weights()])

        # Test the keras model
        self._test_keras_model(model)
test_keras2_numeric.py 文件源码 项目:coremltools 作者: apple 项目源码 文件源码 阅读 21 收藏 0 点赞 0 评论 0
def test_tiny_conv_crop_1d_random(self, model_precision=_MLMODEL_FULL_PRECISION):
        np.random.seed(1988)
        input_dim = 2
        input_length = 10
        filter_length = 3
        nb_filters = 4
        model = Sequential()
        model.add(Conv1D(nb_filters, kernel_size = filter_length, padding='same',
            input_shape=(input_length, input_dim)))
        model.add(Cropping1D(cropping = 2))

        # Set some random weights
        model.set_weights([np.random.rand(*w.shape) for w in model.get_weights()])

        # Test the keras model
        self._test_keras_model(model, model_precision=model_precision)
test_keras2_numeric.py 文件源码 项目:coremltools 作者: apple 项目源码 文件源码 阅读 25 收藏 0 点赞 0 评论 0
def test_tiny_conv_pad_1d_random(self, model_precision=_MLMODEL_FULL_PRECISION):
        np.random.seed(1988)
        input_dim = 2
        input_length = 10
        filter_length = 3
        nb_filters = 4
        model = Sequential()
        model.add(Conv1D(nb_filters, kernel_size = filter_length, padding='same',
            input_shape=(input_length, input_dim)))
        model.add(ZeroPadding1D(padding = 2))

        # Set some random weights
        model.set_weights([np.random.rand(*w.shape) for w in model.get_weights()])

        # Test the keras model
        self._test_keras_model(model, model_precision=model_precision)
dna.py 文件源码 项目:deepcpg 作者: cangermueller 项目源码 文件源码 阅读 26 收藏 0 点赞 0 评论 0
def __call__(self, inputs):
        x = inputs[0]

        kernel_regularizer = kr.L1L2(self.l1_decay, self.l2_decay)
        x = kl.Conv1D(128, 11,
                      kernel_initializer=self.init,
                      kernel_regularizer=kernel_regularizer)(x)
        x = kl.Activation('relu')(x)
        x = kl.MaxPooling1D(4)(x)

        x = kl.Flatten()(x)

        kernel_regularizer = kr.L1L2(l1=self.l1_decay, l2=self.l2_decay)
        x = kl.Dense(self.nb_hidden,
                     kernel_initializer=self.init,
                     kernel_regularizer=kernel_regularizer)(x)
        x = kl.Activation('relu')(x)
        x = kl.Dropout(self.dropout)(x)

        return self._build(inputs, x)
dna.py 文件源码 项目:deepcpg 作者: cangermueller 项目源码 文件源码 阅读 27 收藏 0 点赞 0 评论 0
def __call__(self, inputs):
        x = inputs[0]

        kernel_regularizer = kr.L1L2(l1=self.l1_decay, l2=self.l2_decay)
        x = kl.Conv1D(128, 11,
                      kernel_initializer=self.init,
                      kernel_regularizer=kernel_regularizer)(x)
        x = kl.Activation('relu')(x)
        x = kl.MaxPooling1D(4)(x)

        kernel_regularizer = kr.L1L2(l1=self.l1_decay, l2=self.l2_decay)
        x = kl.Conv1D(256, 7,
                      kernel_initializer=self.init,
                      kernel_regularizer=kernel_regularizer)(x)
        x = kl.Activation('relu')(x)
        x = kl.MaxPooling1D(4)(x)

        kernel_regularizer = kr.L1L2(l1=self.l1_decay, l2=self.l2_decay)
        gru = kl.recurrent.GRU(256, kernel_regularizer=kernel_regularizer)
        x = kl.Bidirectional(gru)(x)
        x = kl.Dropout(self.dropout)(x)

        return self._build(inputs, x)
kaggleQQCharCNNPlus.py 文件源码 项目:kaggle-quora-question-pairs 作者: voletiv 项目源码 文件源码 阅读 19 收藏 0 点赞 0 评论 0
def createBaseNetworkSmall(inputDim, inputLength):
    baseNetwork = Sequential()
    baseNetwork.add(Embedding(input_dim=inputDim,
                              output_dim=inputDim, input_length=inputLength))
    baseNetwork.add(Conv1D(256, 7, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
        mean=0.0, stddev=0.05), bias_initializer=RandomNormal(mean=0.0, stddev=0.05)))
    baseNetwork.add(MaxPooling1D(pool_size=3, strides=3))
    baseNetwork.add(Conv1D(256, 7, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
        mean=0.0, stddev=0.05), bias_initializer=RandomNormal(mean=0.0, stddev=0.05)))
    baseNetwork.add(MaxPooling1D(pool_size=3, strides=3))
    baseNetwork.add(Conv1D(256, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
        mean=0.0, stddev=0.05), bias_initializer=RandomNormal(mean=0.0, stddev=0.05)))
    baseNetwork.add(Conv1D(256, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
        mean=0.0, stddev=0.05), bias_initializer=RandomNormal(mean=0.0, stddev=0.05)))
    baseNetwork.add(Conv1D(256, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
        mean=0.0, stddev=0.05), bias_initializer=RandomNormal(mean=0.0, stddev=0.05)))
    baseNetwork.add(Conv1D(256, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
        mean=0.0, stddev=0.05), bias_initializer=RandomNormal(mean=0.0, stddev=0.05)))
    baseNetwork.add(MaxPooling1D(pool_size=3, strides=3))
    baseNetwork.add(Flatten())
    baseNetwork.add(Dense(1024, activation='relu'))
    baseNetwork.add(Dropout(0.5))
    baseNetwork.add(Dense(1024, activation='relu'))
    baseNetwork.add(Dropout(0.5))
    return baseNetwork
kaggleQQCharCNNPlus.py 文件源码 项目:kaggle-quora-question-pairs 作者: voletiv 项目源码 文件源码 阅读 20 收藏 0 点赞 0 评论 0
def createBaseNetworkLarge(inputDim, inputLength):
    baseNetwork = Sequential()
    baseNetwork.add(Embedding(input_dim=inputDim,
                              output_dim=inputDim, input_length=inputLength))
    baseNetwork.add(Conv1D(1024, 7, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
        mean=0.0, stddev=0.02), bias_initializer=RandomNormal(mean=0.0, stddev=0.02)))
    baseNetwork.add(MaxPooling1D(pool_size=3, strides=3))
    baseNetwork.add(Conv1D(1024, 7, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
        mean=0.0, stddev=0.02), bias_initializer=RandomNormal(mean=0.0, stddev=0.02)))
    baseNetwork.add(MaxPooling1D(pool_size=3, strides=3))
    baseNetwork.add(Conv1D(1024, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
        mean=0.0, stddev=0.02), bias_initializer=RandomNormal(mean=0.0, stddev=0.02)))
    baseNetwork.add(Conv1D(1024, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
        mean=0.0, stddev=0.02), bias_initializer=RandomNormal(mean=0.0, stddev=0.02)))
    baseNetwork.add(Conv1D(1024, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
        mean=0.0, stddev=0.02), bias_initializer=RandomNormal(mean=0.0, stddev=0.02)))
    baseNetwork.add(Conv1D(1024, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
        mean=0.0, stddev=0.02), bias_initializer=RandomNormal(mean=0.0, stddev=0.02)))
    baseNetwork.add(MaxPooling1D(pool_size=3, strides=3))
    baseNetwork.add(Flatten())
    baseNetwork.add(Dense(2048, activation='relu'))
    baseNetwork.add(Dropout(0.5))
    baseNetwork.add(Dense(2048, activation='relu'))
    baseNetwork.add(Dropout(0.5))
    return baseNetwork
kaggleQQDistRMS_CL.py 文件源码 项目:kaggle-quora-question-pairs 作者: voletiv 项目源码 文件源码 阅读 22 收藏 0 点赞 0 评论 0
def createBaseNetworkSmall(inputDim, inputLength):
    baseNetwork = Sequential()
    baseNetwork.add(Embedding(input_dim=inputDim,
                              output_dim=inputDim, input_length=inputLength))
    baseNetwork.add(Conv1D(256, 7, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
        mean=0.0, stddev=0.05), bias_initializer=RandomNormal(mean=0.0, stddev=0.05)))
    baseNetwork.add(MaxPooling1D(pool_size=3, strides=3))
    baseNetwork.add(Conv1D(256, 7, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
        mean=0.0, stddev=0.05), bias_initializer=RandomNormal(mean=0.0, stddev=0.05)))
    baseNetwork.add(MaxPooling1D(pool_size=3, strides=3))
    baseNetwork.add(Conv1D(256, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
        mean=0.0, stddev=0.05), bias_initializer=RandomNormal(mean=0.0, stddev=0.05)))
    baseNetwork.add(Conv1D(256, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
        mean=0.0, stddev=0.05), bias_initializer=RandomNormal(mean=0.0, stddev=0.05)))
    baseNetwork.add(Conv1D(256, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
        mean=0.0, stddev=0.05), bias_initializer=RandomNormal(mean=0.0, stddev=0.05)))
    baseNetwork.add(Conv1D(256, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
        mean=0.0, stddev=0.05), bias_initializer=RandomNormal(mean=0.0, stddev=0.05)))
    baseNetwork.add(MaxPooling1D(pool_size=3, strides=3))
    baseNetwork.add(Flatten())
    baseNetwork.add(Dense(1024, activation='relu'))
    baseNetwork.add(Dropout(0.5))
    baseNetwork.add(Dense(1024, activation='relu'))
    baseNetwork.add(Dropout(0.5))
    return baseNetwork
kaggleQQDistRMS_CL.py 文件源码 项目:kaggle-quora-question-pairs 作者: voletiv 项目源码 文件源码 阅读 22 收藏 0 点赞 0 评论 0
def createBaseNetworkLarge(inputDim, inputLength):
    baseNetwork = Sequential()
    baseNetwork.add(Embedding(input_dim=inputDim,
                              output_dim=inputDim, input_length=inputLength))
    baseNetwork.add(Conv1D(1024, 7, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
        mean=0.0, stddev=0.02), bias_initializer=RandomNormal(mean=0.0, stddev=0.02)))
    baseNetwork.add(MaxPooling1D(pool_size=3, strides=3))
    baseNetwork.add(Conv1D(1024, 7, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
        mean=0.0, stddev=0.02), bias_initializer=RandomNormal(mean=0.0, stddev=0.02)))
    baseNetwork.add(MaxPooling1D(pool_size=3, strides=3))
    baseNetwork.add(Conv1D(1024, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
        mean=0.0, stddev=0.02), bias_initializer=RandomNormal(mean=0.0, stddev=0.02)))
    baseNetwork.add(Conv1D(1024, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
        mean=0.0, stddev=0.02), bias_initializer=RandomNormal(mean=0.0, stddev=0.02)))
    baseNetwork.add(Conv1D(1024, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
        mean=0.0, stddev=0.02), bias_initializer=RandomNormal(mean=0.0, stddev=0.02)))
    baseNetwork.add(Conv1D(1024, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
        mean=0.0, stddev=0.02), bias_initializer=RandomNormal(mean=0.0, stddev=0.02)))
    baseNetwork.add(MaxPooling1D(pool_size=3, strides=3))
    baseNetwork.add(Flatten())
    baseNetwork.add(Dense(2048, activation='relu'))
    baseNetwork.add(Dropout(0.5))
    baseNetwork.add(Dense(2048, activation='relu'))
    baseNetwork.add(Dropout(0.5))
    return baseNetwork
kaggleQQSigmoid_SG_smallerAlphabet.py 文件源码 项目:kaggle-quora-question-pairs 作者: voletiv 项目源码 文件源码 阅读 21 收藏 0 点赞 0 评论 0
def createBaseNetworkSmall(inputLength, inputDim):
    baseNetwork = Sequential()
    baseNetwork.add(Conv1D(256, 7, strides=1, padding='valid', activation='relu', input_shape=(inputLength, inputDim),
                           kernel_initializer=RandomNormal(mean=0.0, stddev=0.05), bias_initializer=RandomNormal(mean=0.0, stddev=0.05)))
    baseNetwork.add(MaxPooling1D(pool_size=3, strides=3))
    baseNetwork.add(Conv1D(256, 7, strides=1, padding='valid', activation='relu',
                           kernel_initializer=RandomNormal(mean=0.0, stddev=0.05), bias_initializer=RandomNormal(mean=0.0, stddev=0.05)))
    baseNetwork.add(MaxPooling1D(pool_size=3, strides=3))
    baseNetwork.add(Conv1D(256, 3, strides=1, padding='valid', activation='relu',
                           kernel_initializer=RandomNormal(mean=0.0, stddev=0.05), bias_initializer=RandomNormal(mean=0.0, stddev=0.05)))
    baseNetwork.add(Conv1D(256, 3, strides=1, padding='valid', activation='relu',
                           kernel_initializer=RandomNormal(mean=0.0, stddev=0.05), bias_initializer=RandomNormal(mean=0.0, stddev=0.05)))
    baseNetwork.add(Conv1D(256, 3, strides=1, padding='valid', activation='relu',
                           kernel_initializer=RandomNormal(mean=0.0, stddev=0.05), bias_initializer=RandomNormal(mean=0.0, stddev=0.05)))
    baseNetwork.add(Conv1D(256, 3, strides=1, padding='valid', activation='relu',
                           kernel_initializer=RandomNormal(mean=0.0, stddev=0.05), bias_initializer=RandomNormal(mean=0.0, stddev=0.05)))
    baseNetwork.add(MaxPooling1D(pool_size=3, strides=3))
    baseNetwork.add(Flatten())
    baseNetwork.add(Dense(1024, activation='relu'))
    baseNetwork.add(Dropout(0.5))
    baseNetwork.add(Dense(1024, activation='relu'))
    baseNetwork.add(Dropout(0.5))
    return baseNetwork
kaggleQQSigmoid_SG_BCE.py 文件源码 项目:kaggle-quora-question-pairs 作者: voletiv 项目源码 文件源码 阅读 22 收藏 0 点赞 0 评论 0
def createBaseNetworkSmall(inputLength, inputDim):
    baseNetwork = Sequential()
    baseNetwork.add(Conv1D(256, 7, strides=1, padding='valid', activation='relu',  input_shape=(inputLength, inputDim),
                           kernel_initializer=RandomNormal(mean=0.0, stddev=0.05), bias_initializer=RandomNormal(mean=0.0, stddev=0.05)))
    baseNetwork.add(MaxPooling1D(pool_size=3, strides=3))
    baseNetwork.add(Conv1D(256, 7, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
        mean=0.0, stddev=0.05), bias_initializer=RandomNormal(mean=0.0, stddev=0.05)))
    baseNetwork.add(MaxPooling1D(pool_size=3, strides=3))
    baseNetwork.add(Conv1D(256, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
        mean=0.0, stddev=0.05), bias_initializer=RandomNormal(mean=0.0, stddev=0.05)))
    baseNetwork.add(Conv1D(256, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
        mean=0.0, stddev=0.05), bias_initializer=RandomNormal(mean=0.0, stddev=0.05)))
    baseNetwork.add(Conv1D(256, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
        mean=0.0, stddev=0.05), bias_initializer=RandomNormal(mean=0.0, stddev=0.05)))
    baseNetwork.add(Conv1D(256, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
        mean=0.0, stddev=0.05), bias_initializer=RandomNormal(mean=0.0, stddev=0.05)))
    baseNetwork.add(MaxPooling1D(pool_size=3, strides=3))
    baseNetwork.add(Flatten())
    baseNetwork.add(Dense(1024, activation='relu'))
    baseNetwork.add(Dropout(0.5))
    baseNetwork.add(Dense(1024, activation='relu'))
    baseNetwork.add(Dropout(0.5))
    return baseNetwork
kaggleQQSigmoid_SG_BCE.py 文件源码 项目:kaggle-quora-question-pairs 作者: voletiv 项目源码 文件源码 阅读 26 收藏 0 点赞 0 评论 0
def createBaseNetworkLarge(inputLength, inputDim):
    baseNetwork = Sequential()
    baseNetwork.add(Conv1D(1024, 7, strides=1, padding='valid', activation='relu',  input_shape=(inputLength, inputDim),
                           kernel_initializer=RandomNormal(mean=0.0, stddev=0.02), bias_initializer=RandomNormal(mean=0.0, stddev=0.02)))
    baseNetwork.add(MaxPooling1D(pool_size=3, strides=3))
    baseNetwork.add(Conv1D(1024, 7, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
        mean=0.0, stddev=0.02), bias_initializer=RandomNormal(mean=0.0, stddev=0.02)))
    baseNetwork.add(MaxPooling1D(pool_size=3, strides=3))
    baseNetwork.add(Conv1D(1024, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
        mean=0.0, stddev=0.02), bias_initializer=RandomNormal(mean=0.0, stddev=0.02)))
    baseNetwork.add(Conv1D(1024, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
        mean=0.0, stddev=0.02), bias_initializer=RandomNormal(mean=0.0, stddev=0.02)))
    baseNetwork.add(Conv1D(1024, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
        mean=0.0, stddev=0.02), bias_initializer=RandomNormal(mean=0.0, stddev=0.02)))
    baseNetwork.add(Conv1D(1024, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
        mean=0.0, stddev=0.02), bias_initializer=RandomNormal(mean=0.0, stddev=0.02)))
    baseNetwork.add(MaxPooling1D(pool_size=3, strides=3))
    baseNetwork.add(Flatten())
    baseNetwork.add(Dense(2048, activation='relu'))
    baseNetwork.add(Dropout(0.5))
    baseNetwork.add(Dense(2048, activation='relu'))
    baseNetwork.add(Dropout(0.5))
    return baseNetwork
kaggleQQDistSG_CL.py 文件源码 项目:kaggle-quora-question-pairs 作者: voletiv 项目源码 文件源码 阅读 22 收藏 0 点赞 0 评论 0
def createBaseNetworkSmall(inputDim, inputLength):
    baseNetwork = Sequential()
    baseNetwork.add(Embedding(input_dim=inputDim,
                              output_dim=inputDim, input_length=inputLength))
    baseNetwork.add(Conv1D(256, 7, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
        mean=0.0, stddev=0.05), bias_initializer=RandomNormal(mean=0.0, stddev=0.05)))
    baseNetwork.add(MaxPooling1D(pool_size=3, strides=3))
    baseNetwork.add(Conv1D(256, 7, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
        mean=0.0, stddev=0.05), bias_initializer=RandomNormal(mean=0.0, stddev=0.05)))
    baseNetwork.add(MaxPooling1D(pool_size=3, strides=3))
    baseNetwork.add(Conv1D(256, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
        mean=0.0, stddev=0.05), bias_initializer=RandomNormal(mean=0.0, stddev=0.05)))
    baseNetwork.add(Conv1D(256, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
        mean=0.0, stddev=0.05), bias_initializer=RandomNormal(mean=0.0, stddev=0.05)))
    baseNetwork.add(Conv1D(256, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
        mean=0.0, stddev=0.05), bias_initializer=RandomNormal(mean=0.0, stddev=0.05)))
    baseNetwork.add(Conv1D(256, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
        mean=0.0, stddev=0.05), bias_initializer=RandomNormal(mean=0.0, stddev=0.05)))
    baseNetwork.add(MaxPooling1D(pool_size=3, strides=3))
    baseNetwork.add(Flatten())
    baseNetwork.add(Dense(1024, activation='relu'))
    baseNetwork.add(Dropout(0.5))
    baseNetwork.add(Dense(1024, activation='relu'))
    baseNetwork.add(Dropout(0.5))
    return baseNetwork
kaggleQQDistSG_CL.py 文件源码 项目:kaggle-quora-question-pairs 作者: voletiv 项目源码 文件源码 阅读 24 收藏 0 点赞 0 评论 0
def createBaseNetworkLarge(inputDim, inputLength):
    baseNetwork = Sequential()
    baseNetwork.add(Embedding(input_dim=inputDim,
                              output_dim=inputDim, input_length=inputLength))
    baseNetwork.add(Conv1D(1024, 7, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
        mean=0.0, stddev=0.02), bias_initializer=RandomNormal(mean=0.0, stddev=0.02)))
    baseNetwork.add(MaxPooling1D(pool_size=3, strides=3))
    baseNetwork.add(Conv1D(1024, 7, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
        mean=0.0, stddev=0.02), bias_initializer=RandomNormal(mean=0.0, stddev=0.02)))
    baseNetwork.add(MaxPooling1D(pool_size=3, strides=3))
    baseNetwork.add(Conv1D(1024, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
        mean=0.0, stddev=0.02), bias_initializer=RandomNormal(mean=0.0, stddev=0.02)))
    baseNetwork.add(Conv1D(1024, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
        mean=0.0, stddev=0.02), bias_initializer=RandomNormal(mean=0.0, stddev=0.02)))
    baseNetwork.add(Conv1D(1024, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
        mean=0.0, stddev=0.02), bias_initializer=RandomNormal(mean=0.0, stddev=0.02)))
    baseNetwork.add(Conv1D(1024, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
        mean=0.0, stddev=0.02), bias_initializer=RandomNormal(mean=0.0, stddev=0.02)))
    baseNetwork.add(MaxPooling1D(pool_size=3, strides=3))
    baseNetwork.add(Flatten())
    baseNetwork.add(Dense(2048, activation='relu'))
    baseNetwork.add(Dropout(0.5))
    baseNetwork.add(Dense(2048, activation='relu'))
    baseNetwork.add(Dropout(0.5))
    return baseNetwork
testSigmoid.py 文件源码 项目:kaggle-quora-question-pairs 作者: voletiv 项目源码 文件源码 阅读 27 收藏 0 点赞 0 评论 0
def createBaseNetworkLarge(inputDim, inputLength):
    baseNetwork = Sequential()
    baseNetwork.add(Conv1D(1024, 7, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
        mean=0.0, stddev=0.02), bias_initializer=RandomNormal(mean=0.0, stddev=0.02)))
    baseNetwork.add(MaxPooling1D(pool_size=3, strides=3))
    baseNetwork.add(Conv1D(1024, 7, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
        mean=0.0, stddev=0.02), bias_initializer=RandomNormal(mean=0.0, stddev=0.02)))
    baseNetwork.add(MaxPooling1D(pool_size=3, strides=3))
    baseNetwork.add(Conv1D(1024, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
        mean=0.0, stddev=0.02), bias_initializer=RandomNormal(mean=0.0, stddev=0.02)))
    baseNetwork.add(Conv1D(1024, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
        mean=0.0, stddev=0.02), bias_initializer=RandomNormal(mean=0.0, stddev=0.02)))
    baseNetwork.add(Conv1D(1024, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
        mean=0.0, stddev=0.02), bias_initializer=RandomNormal(mean=0.0, stddev=0.02)))
    baseNetwork.add(Conv1D(1024, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
        mean=0.0, stddev=0.02), bias_initializer=RandomNormal(mean=0.0, stddev=0.02)))
    baseNetwork.add(MaxPooling1D(pool_size=3, strides=3))
    baseNetwork.add(Flatten())
    baseNetwork.add(Dense(2048, activation='relu'))
    baseNetwork.add(Dropout(0.5))
    baseNetwork.add(Dense(2048, activation='relu'))
    baseNetwork.add(Dropout(0.5))
    return baseNetwork
testSigmoidSmaller.py 文件源码 项目:kaggle-quora-question-pairs 作者: voletiv 项目源码 文件源码 阅读 26 收藏 0 点赞 0 评论 0
def netSigmoid(inputLength, inputDim):
    baseNetwork = Sequential()
    baseNetwork.add(Conv1D(256, 7, strides=1, padding='valid', activation='relu',  input_shape=(inputLength, inputDim),
                           kernel_initializer=RandomNormal(mean=0.0, stddev=0.05), bias_initializer=RandomNormal(mean=0.0, stddev=0.05)))
    baseNetwork.add(MaxPooling1D(pool_size=3, strides=3))
    baseNetwork.add(Conv1D(256, 7, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
        mean=0.0, stddev=0.05), bias_initializer=RandomNormal(mean=0.0, stddev=0.05)))
    baseNetwork.add(MaxPooling1D(pool_size=3, strides=3))
    baseNetwork.add(Conv1D(256, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
        mean=0.0, stddev=0.05), bias_initializer=RandomNormal(mean=0.0, stddev=0.05)))
    baseNetwork.add(Conv1D(256, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
        mean=0.0, stddev=0.05), bias_initializer=RandomNormal(mean=0.0, stddev=0.05)))
    baseNetwork.add(Conv1D(256, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
        mean=0.0, stddev=0.05), bias_initializer=RandomNormal(mean=0.0, stddev=0.05)))
    baseNetwork.add(Conv1D(256, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
        mean=0.0, stddev=0.05), bias_initializer=RandomNormal(mean=0.0, stddev=0.05)))
    baseNetwork.add(MaxPooling1D(pool_size=3, strides=3))
    baseNetwork.add(Flatten())
    baseNetwork.add(Dense(1024, activation='relu'))
    baseNetwork.add(Dropout(0.5))
    baseNetwork.add(Dense(1024, activation='relu'))
    baseNetwork.add(Dropout(0.5))
    return baseNetwork
kaggleQQ_Euc.py 文件源码 项目:kaggle-quora-question-pairs 作者: voletiv 项目源码 文件源码 阅读 26 收藏 0 点赞 0 评论 0
def createBaseNetworkSmall(inputLength, inputDim):
    baseNetwork = Sequential()
    baseNetwork.add(Conv1D(256, 7, strides=1, padding='valid', activation='relu',  input_shape=(inputLength, inputDim),
                           kernel_initializer=RandomNormal(mean=0.0, stddev=0.05), bias_initializer=RandomNormal(mean=0.0, stddev=0.05)))
    baseNetwork.add(MaxPooling1D(pool_size=3, strides=3))
    baseNetwork.add(Conv1D(256, 7, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
        mean=0.0, stddev=0.05), bias_initializer=RandomNormal(mean=0.0, stddev=0.05)))
    baseNetwork.add(MaxPooling1D(pool_size=3, strides=3))
    baseNetwork.add(Conv1D(256, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
        mean=0.0, stddev=0.05), bias_initializer=RandomNormal(mean=0.0, stddev=0.05)))
    baseNetwork.add(Conv1D(256, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
        mean=0.0, stddev=0.05), bias_initializer=RandomNormal(mean=0.0, stddev=0.05)))
    baseNetwork.add(Conv1D(256, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
        mean=0.0, stddev=0.05), bias_initializer=RandomNormal(mean=0.0, stddev=0.05)))
    baseNetwork.add(Conv1D(256, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
        mean=0.0, stddev=0.05), bias_initializer=RandomNormal(mean=0.0, stddev=0.05)))
    baseNetwork.add(MaxPooling1D(pool_size=3, strides=3))
    baseNetwork.add(Flatten())
    baseNetwork.add(Dense(1024, activation='relu'))
    baseNetwork.add(Dropout(0.5))
    baseNetwork.add(Dense(1024, activation='relu'))
    baseNetwork.add(Dropout(0.5))
    return baseNetwork
test_Euc_Small.py 文件源码 项目:kaggle-quora-question-pairs 作者: voletiv 项目源码 文件源码 阅读 31 收藏 0 点赞 0 评论 0
def createBaseNetworkSmall(inputLength, inputDim):
    baseNetwork = Sequential()
    baseNetwork.add(Conv1D(256, 7, strides=1, padding='valid', activation='relu',  input_shape=(inputLength, inputDim),
                           kernel_initializer=RandomNormal(mean=0.0, stddev=0.05), bias_initializer=RandomNormal(mean=0.0, stddev=0.05)))
    baseNetwork.add(MaxPooling1D(pool_size=3, strides=3))
    baseNetwork.add(Conv1D(256, 7, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
        mean=0.0, stddev=0.05), bias_initializer=RandomNormal(mean=0.0, stddev=0.05)))
    baseNetwork.add(MaxPooling1D(pool_size=3, strides=3))
    baseNetwork.add(Conv1D(256, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
        mean=0.0, stddev=0.05), bias_initializer=RandomNormal(mean=0.0, stddev=0.05)))
    baseNetwork.add(MaxPooling1D(pool_size=3, strides=3))
    baseNetwork.add(Flatten())
    baseNetwork.add(Dense(128, activation='relu'))
    baseNetwork.add(Dropout(0.2))
    baseNetwork.add(Dense(128, activation='relu'))
    baseNetwork.add(Dropout(0.2))
    return baseNetwork
test.py 文件源码 项目:kaggle-quora-question-pairs 作者: voletiv 项目源码 文件源码 阅读 20 收藏 0 点赞 0 评论 0
def createBaseNetworkSmall(inputDim, inputLength):
        baseNetwork = Sequential()
        baseNetwork.add(Embedding(input_dim=inputDim, output_dim=inputDim, input_length=inputLength))
        baseNetwork.add(Conv1D(256, 7, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(mean=0.0, stddev=0.05), bias_initializer=RandomNormal(mean=0.0, stddev=0.05)))
        baseNetwork.add(MaxPooling1D(pool_size=3, strides=3))
        baseNetwork.add(Conv1D(256, 7, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(mean=0.0, stddev=0.05), bias_initializer=RandomNormal(mean=0.0, stddev=0.05)))
        baseNetwork.add(MaxPooling1D(pool_size=3, strides=3))
        baseNetwork.add(Conv1D(256, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(mean=0.0, stddev=0.05), bias_initializer=RandomNormal(mean=0.0, stddev=0.05)))
        baseNetwork.add(Conv1D(256, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(mean=0.0, stddev=0.05), bias_initializer=RandomNormal(mean=0.0, stddev=0.05)))
        baseNetwork.add(Conv1D(256, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(mean=0.0, stddev=0.05), bias_initializer=RandomNormal(mean=0.0, stddev=0.05)))
        baseNetwork.add(Conv1D(256, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(mean=0.0, stddev=0.05), bias_initializer=RandomNormal(mean=0.0, stddev=0.05)))
        baseNetwork.add(MaxPooling1D(pool_size=3, strides=3))
        baseNetwork.add(Flatten())
        baseNetwork.add(Dense(1024, activation='relu'))
        baseNetwork.add(Dropout(0.5))
        baseNetwork.add(Dense(1024, activation='relu'))
        baseNetwork.add(Dropout(0.5))
        return baseNetwork
test.py 文件源码 项目:kaggle-quora-question-pairs 作者: voletiv 项目源码 文件源码 阅读 18 收藏 0 点赞 0 评论 0
def createBaseNetworkLarge(inputDim, inputLength):
        baseNetwork = Sequential()
        baseNetwork.add(Embedding(input_dim=inputDim, output_dim=inputDim, input_length=inputLength))
        baseNetwork.add(Conv1D(1024, 7, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(mean=0.0, stddev=0.02), bias_initializer=RandomNormal(mean=0.0, stddev=0.02)))
        baseNetwork.add(MaxPooling1D(pool_size=3, strides=3))
        baseNetwork.add(Conv1D(1024, 7, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(mean=0.0, stddev=0.02), bias_initializer=RandomNormal(mean=0.0, stddev=0.02)))
        baseNetwork.add(MaxPooling1D(pool_size=3, strides=3))
        baseNetwork.add(Conv1D(1024, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(mean=0.0, stddev=0.02), bias_initializer=RandomNormal(mean=0.0, stddev=0.02)))
        baseNetwork.add(Conv1D(1024, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(mean=0.0, stddev=0.02), bias_initializer=RandomNormal(mean=0.0, stddev=0.02)))
        baseNetwork.add(Conv1D(1024, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(mean=0.0, stddev=0.02), bias_initializer=RandomNormal(mean=0.0, stddev=0.02)))
        baseNetwork.add(Conv1D(1024, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(mean=0.0, stddev=0.02), bias_initializer=RandomNormal(mean=0.0, stddev=0.02)))
        baseNetwork.add(MaxPooling1D(pool_size=3, strides=3))
        baseNetwork.add(Flatten())
        baseNetwork.add(Dense(2048, activation='relu'))
        baseNetwork.add(Dropout(0.5))
        baseNetwork.add(Dense(2048, activation='relu'))
        baseNetwork.add(Dropout(0.5))
        return baseNetwork
kaggleQQ_Euc_Small.py 文件源码 项目:kaggle-quora-question-pairs 作者: voletiv 项目源码 文件源码 阅读 18 收藏 0 点赞 0 评论 0
def createBaseNetworkSmall(inputLength, inputDim):
    baseNetwork = Sequential()
    baseNetwork.add(Conv1D(256, 7, strides=1, padding='valid', activation='relu',  input_shape=(inputLength, inputDim),
                           kernel_initializer=RandomNormal(mean=0.0, stddev=0.05), bias_initializer=RandomNormal(mean=0.0, stddev=0.05)))
    baseNetwork.add(MaxPooling1D(pool_size=3, strides=3))
    baseNetwork.add(Conv1D(256, 7, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
        mean=0.0, stddev=0.05), bias_initializer=RandomNormal(mean=0.0, stddev=0.05)))
    baseNetwork.add(MaxPooling1D(pool_size=3, strides=3))
    baseNetwork.add(Conv1D(256, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
        mean=0.0, stddev=0.05), bias_initializer=RandomNormal(mean=0.0, stddev=0.05)))
    baseNetwork.add(MaxPooling1D(pool_size=3, strides=3))
    baseNetwork.add(Flatten())
    baseNetwork.add(Dense(128, activation='relu'))
    baseNetwork.add(Dropout(0.2))
    baseNetwork.add(Dense(128, activation='relu'))
    baseNetwork.add(Dropout(0.2))
    return baseNetwork


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