8 examples of 'pytorch dropout example' in Python

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def test_dropout(self):
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    input_sample_shape = (64, 1, 12)
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    dropout = layer.Dropout('drop', input_sample_shape=input_sample_shape)
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    out_sample_shape = dropout.get_output_sample_shape()
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    self.check_shape(out_sample_shape, input_sample_shape)

8
def dropout(x, ratio=.5, **kwargs):
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    """dropout regularization
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    Even though it scales its input at training,
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    we do not consider it in Lipschitz constant.
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    :param x: input (vector/tensor, label, lipschitz)
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    :param ratio: dropout ratio
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    :return:
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    """
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    x, t, l = x
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    x = chainer.functions.dropout(x, ratio=ratio, **kwargs)
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    return x, t, l

76
def dropout(input, *args, **kwargs):
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  output = F.dropout(input.F, *args, **kwargs)
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  return SparseTensor(output, coords_key=input.coords_key, coords_manager=input.coords_man)

69
def forward(self, input_tensor):
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    emb = self.encoder_with_dropout(input_tensor, dropout=self.dropout_embedding if self.training else 0)
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    return self.dropout_input(emb)

192
def dropout(x, keep_prob):
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	""" During training, performs dropout. Otherwise, returns original."""
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	output = tf.nn.dropout(x, keep_prob) if drop else x
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	return output

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@hparams(default=0.0, required=False)
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def dropout(self):
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    pass

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def Dropout(x, rate, training) :
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    return tf.layers.dropout(inputs=x, rate=rate, training=training)

346
def test_dropout():
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    """Tests whether dropout layer reads in probability correctly"""
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    rnn = RNN(layers_info=[["lstm", 20], ["gru", 10], ["linear", 20], ["linear", 1]],
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                           hidden_activations="relu", output_activation="sigmoid", dropout=0.9999,
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                           initialiser="xavier")
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    assert rnn.dropout_layer.rate == 0.9999
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    assert not solves_simple_problem(X, y, rnn)
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    rnn = RNN(layers_info=[["lstm", 20], ["gru", 10], ["linear", 20], ["linear", 1]],
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                           hidden_activations="relu", output_activation=None, dropout=0.0000001,
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                           initialiser="xavier")
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    assert rnn.dropout_layer.rate == 0.0000001
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    assert solves_simple_problem(X, y, rnn)