Python cntk.tanh() Examples

The following are 12 code examples of cntk.tanh(). 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 cntk , or try the search function

Example #1

Source File: test_ops_unary.py From ngraph-python with Apache License 2.0

5 votes

def test_tanh():
    assert_cntk_ngraph_isclose(C.tanh([-2, -1., 0., 1., 2.]))
    assert_cntk_ngraph_isclose(C.tanh([0.]))
    assert_cntk_ngraph_isclose(C.tanh([-0.9, -0.8, -0.7, -0.6, -0.5, -0.4, -0.3, -0.2, -0.1, 0.]))

Example #2

Source File: cntk_backend.py From GraphicDesignPatternByPython with MIT License

5 votes

def tanh(x):
    return C.tanh(x)

Example #3

Source File: train_end2end.py From end2end_AU_speech with MIT License

5 votes

def create_model(input, net_type="gru", encoder_type=1, model_file=None, e3cloning=False):
    if encoder_type == 1:
        h = audio_encoder(input)
        if net_type.lower() is not "cnn":
            h = flatten(h)
    elif encoder_type == 2:
        h = audio_encoder_2(input)
        # pooling
        h = C.layers.GlobalAveragePooling(name="avgpool")(h)
        h = C.squeeze(h)
    elif encoder_type == 3:
        h = audio_encoder_3(input, model_file, e3cloning)
        if net_type.lower() is not "cnn":
            h = flatten(h)
    else:
        raise ValueError("encoder type {:d} not supported".format(encoder_type))

    if net_type.lower() == "cnn":
        h = C.layers.Dense(1024, init=C.he_normal(), activation=C.tanh)(h)
    elif net_type.lower() == "gru":
        h = C.layers.Recurrence(step_function=C.layers.GRU(256), go_backwards=False, name="rnn")(h)
    elif net_type.lower() == "lstm":
        h = C.layers.Recurrence(step_function=C.layers.LSTM(256), go_backwards=False, name="rnn")(h)
    elif net_type.lower() == "bigru":
        # bi-directional GRU
        h = bi_recurrence(h, C.layers.GRU(128), C.layers.GRU(128), name="bigru")
    elif net_type.lower() == "bilstm":
        # bi-directional LSTM
        h = bi_recurrence(h, C.layers.LSTM(128), C.layers.LSTM(128), name="bilstm")
    h = C.layers.Dropout(0.2)(h)
    # output
    y = C.layers.Dense(label_dim, activation=C.sigmoid, init=C.he_normal(), name="output")(h)
    return y

#--------------------------------------
# loss functions
#--------------------------------------

Example #4