Python keras.layers.Average() Examples

The following are 15 code examples of keras.layers.Average(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module keras.layers , or try the search function .
Example #1
Source Project: DeepLearning_Wavelet-LSTM   Author: hello-sea   File: merge_test.py    License: MIT License 6 votes vote down vote up
def test_merge_average():
    i1 = layers.Input(shape=(4, 5))
    i2 = layers.Input(shape=(4, 5))
    o = layers.average([i1, i2])
    assert o._keras_shape == (None, 4, 5)
    model = models.Model([i1, i2], o)

    avg_layer = layers.Average()
    o2 = avg_layer([i1, i2])
    assert avg_layer.output_shape == (None, 4, 5)

    x1 = np.random.random((2, 4, 5))
    x2 = np.random.random((2, 4, 5))
    out = model.predict([x1, x2])
    assert out.shape == (2, 4, 5)
    assert_allclose(out, 0.5 * (x1 + x2), atol=1e-4) 
Example #2
Source Project: DeepLearning_Wavelet-LSTM   Author: hello-sea   File: merge_test.py    License: MIT License 6 votes vote down vote up
def test_merge_average():
    i1 = layers.Input(shape=(4, 5))
    i2 = layers.Input(shape=(4, 5))
    o = layers.average([i1, i2])
    assert o._keras_shape == (None, 4, 5)
    model = models.Model([i1, i2], o)

    avg_layer = layers.Average()
    o2 = avg_layer([i1, i2])
    assert avg_layer.output_shape == (None, 4, 5)

    x1 = np.random.random((2, 4, 5))
    x2 = np.random.random((2, 4, 5))
    out = model.predict([x1, x2])
    assert out.shape == (2, 4, 5)
    assert_allclose(out, 0.5 * (x1 + x2), atol=1e-4) 
Example #3
Source Project: DeepLearning_Wavelet-LSTM   Author: hello-sea   File: merge_test.py    License: MIT License 6 votes vote down vote up
def test_merge_average():
    i1 = layers.Input(shape=(4, 5))
    i2 = layers.Input(shape=(4, 5))
    o = layers.average([i1, i2])
    assert o._keras_shape == (None, 4, 5)
    model = models.Model([i1, i2], o)

    avg_layer = layers.Average()
    o2 = avg_layer([i1, i2])
    assert avg_layer.output_shape == (None, 4, 5)

    x1 = np.random.random((2, 4, 5))
    x2 = np.random.random((2, 4, 5))
    out = model.predict([x1, x2])
    assert out.shape == (2, 4, 5)
    assert_allclose(out, 0.5 * (x1 + x2), atol=1e-4) 
Example #4
Source Project: DeepLearning_Wavelet-LSTM   Author: hello-sea   File: merge_test.py    License: MIT License 6 votes vote down vote up
def test_merge_average():
    i1 = layers.Input(shape=(4, 5))
    i2 = layers.Input(shape=(4, 5))
    o = layers.average([i1, i2])
    assert o._keras_shape == (None, 4, 5)
    model = models.Model([i1, i2], o)

    avg_layer = layers.Average()
    o2 = avg_layer([i1, i2])
    assert avg_layer.output_shape == (None, 4, 5)

    x1 = np.random.random((2, 4, 5))
    x2 = np.random.random((2, 4, 5))
    out = model.predict([x1, x2])
    assert out.shape == (2, 4, 5)
    assert_allclose(out, 0.5 * (x1 + x2), atol=1e-4) 
Example #5
Source Project: DeepLearning_Wavelet-LSTM   Author: hello-sea   File: merge_test.py    License: MIT License 6 votes vote down vote up
def test_merge_average():
    i1 = layers.Input(shape=(4, 5))
    i2 = layers.Input(shape=(4, 5))
    o = layers.average([i1, i2])
    assert o._keras_shape == (None, 4, 5)
    model = models.Model([i1, i2], o)

    avg_layer = layers.Average()
    o2 = avg_layer([i1, i2])
    assert avg_layer.output_shape == (None, 4, 5)

    x1 = np.random.random((2, 4, 5))
    x2 = np.random.random((2, 4, 5))
    out = model.predict([x1, x2])
    assert out.shape == (2, 4, 5)
    assert_allclose(out, 0.5 * (x1 + x2), atol=1e-4) 
Example #6
Source Project: DeepLearning_Wavelet-LSTM   Author: hello-sea   File: merge_test.py    License: MIT License 6 votes vote down vote up
def test_merge_average():
    i1 = layers.Input(shape=(4, 5))
    i2 = layers.Input(shape=(4, 5))
    o = layers.average([i1, i2])
    assert o._keras_shape == (None, 4, 5)
    model = models.Model([i1, i2], o)

    avg_layer = layers.Average()
    o2 = avg_layer([i1, i2])
    assert avg_layer.output_shape == (None, 4, 5)

    x1 = np.random.random((2, 4, 5))
    x2 = np.random.random((2, 4, 5))
    out = model.predict([x1, x2])
    assert out.shape == (2, 4, 5)
    assert_allclose(out, 0.5 * (x1 + x2), atol=1e-4) 
Example #7
Source Project: semeval2019-hyperpartisan-bertha-von-suttner   Author: GateNLP   File: ensemble_pred.py    License: Apache License 2.0 5 votes vote down vote up
def ensemble(models,model_input):
    outputs = [model(model_input) for model in models]
    y = Average()(outputs)

    model = Model(model_input, y, name='ensemble')

    return model 
Example #8
Source Project: DeepLearning_Wavelet-LSTM   Author: hello-sea   File: regularizers_test.py    License: MIT License 5 votes vote down vote up
def create_multi_input_model_from(layer1, layer2):
    input_1 = Input(shape=(data_dim,))
    input_2 = Input(shape=(data_dim,))
    out1 = layer1(input_1)
    out2 = layer2(input_2)
    out = Average()([out1, out2])
    model = Model([input_1, input_2], out)
    model.add_loss(K.mean(out2))
    model.add_loss(1)
    model.add_loss(1)
    return model 
Example #9
Source Project: DeepLearning_Wavelet-LSTM   Author: hello-sea   File: regularizers_test.py    License: MIT License 5 votes vote down vote up
def create_multi_input_model_from(layer1, layer2):
    input_1 = Input(shape=(data_dim,))
    input_2 = Input(shape=(data_dim,))
    out1 = layer1(input_1)
    out2 = layer2(input_2)
    out = Average()([out1, out2])
    model = Model([input_1, input_2], out)
    model.add_loss(K.mean(out2))
    model.add_loss(1)
    model.add_loss(1)
    return model 
Example #10
Source Project: DeepLearning_Wavelet-LSTM   Author: hello-sea   File: regularizers_test.py    License: MIT License 5 votes vote down vote up
def create_multi_input_model_from(layer1, layer2):
    input_1 = Input(shape=(data_dim,))
    input_2 = Input(shape=(data_dim,))
    out1 = layer1(input_1)
    out2 = layer2(input_2)
    out = Average()([out1, out2])
    model = Model([input_1, input_2], out)
    model.add_loss(K.mean(out2))
    model.add_loss(1)
    model.add_loss(1)
    return model 
Example #11
Source Project: DeepLearning_Wavelet-LSTM   Author: hello-sea   File: regularizers_test.py    License: MIT License 5 votes vote down vote up
def create_multi_input_model_from(layer1, layer2):
    input_1 = Input(shape=(data_dim,))
    input_2 = Input(shape=(data_dim,))
    out1 = layer1(input_1)
    out2 = layer2(input_2)
    out = Average()([out1, out2])
    model = Model([input_1, input_2], out)
    model.add_loss(K.mean(out2))
    model.add_loss(1)
    model.add_loss(1)
    return model 
Example #12
Source Project: DeepLearning_Wavelet-LSTM   Author: hello-sea   File: regularizers_test.py    License: MIT License 5 votes vote down vote up
def create_multi_input_model_from(layer1, layer2):
    input_1 = Input(shape=(data_dim,))
    input_2 = Input(shape=(data_dim,))
    out1 = layer1(input_1)
    out2 = layer2(input_2)
    out = Average()([out1, out2])
    model = Model([input_1, input_2], out)
    model.add_loss(K.mean(out2))
    model.add_loss(1)
    model.add_loss(1)
    return model 
Example #13
Source Project: DeepLearning_Wavelet-LSTM   Author: hello-sea   File: regularizers_test.py    License: MIT License 5 votes vote down vote up
def create_multi_input_model_from(layer1, layer2):
    input_1 = Input(shape=(data_dim,))
    input_2 = Input(shape=(data_dim,))
    out1 = layer1(input_1)
    out2 = layer2(input_2)
    out = Average()([out1, out2])
    model = Model([input_1, input_2], out)
    model.add_loss(K.mean(out2))
    model.add_loss(1)
    model.add_loss(1)
    return model 
Example #14
Source Project: DeepLearning_Wavelet-LSTM   Author: hello-sea   File: regularizers_test.py    License: MIT License 5 votes vote down vote up
def create_multi_input_model_from(layer1, layer2):
    input_1 = Input(shape=(data_dim,))
    input_2 = Input(shape=(data_dim,))
    out1 = layer1(input_1)
    out2 = layer2(input_2)
    out = Average()([out1, out2])
    model = Model([input_1, input_2], out)
    model.add_loss(K.mean(out2))
    model.add_loss(1)
    model.add_loss(1)
    return model 
Example #15
Source Project: DeepLearning_Wavelet-LSTM   Author: hello-sea   File: regularizers_test.py    License: MIT License 5 votes vote down vote up
def create_multi_input_model_from(layer1, layer2):
    input_1 = Input(shape=(data_dim,))
    input_2 = Input(shape=(data_dim,))
    out1 = layer1(input_1)
    out2 = layer2(input_2)
    out = Average()([out1, out2])
    model = Model([input_1, input_2], out)
    model.add_loss(K.mean(out2))
    model.add_loss(1)
    model.add_loss(1)
    return model