8 examples of 'pytorch dropout example' in Python

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179def test_dropout(self):
180 input_sample_shape = (64, 1, 12)
181 dropout = layer.Dropout('drop', input_sample_shape=input_sample_shape)
182 out_sample_shape = dropout.get_output_sample_shape()
183 self.check_shape(out_sample_shape, input_sample_shape)
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8def dropout(x, ratio=.5, **kwargs):
9 """dropout regularization
10 Even though it scales its input at training,
11 we do not consider it in Lipschitz constant.
12
13 :param x: input (vector/tensor, label, lipschitz)
14 :param ratio: dropout ratio
15 :return:
16 """
17 x, t, l = x
18 x = chainer.functions.dropout(x, ratio=ratio, **kwargs)
19 return x, t, l
76def dropout(input, *args, **kwargs):
77 output = F.dropout(input.F, *args, **kwargs)
78 return SparseTensor(output, coords_key=input.coords_key, coords_manager=input.coords_man)
69def forward(self, input_tensor):
70 emb = self.encoder_with_dropout(input_tensor, dropout=self.dropout_embedding if self.training else 0)
71 return self.dropout_input(emb)
192def dropout(x, keep_prob):
193 """ During training, performs dropout. Otherwise, returns original."""
194 output = tf.nn.dropout(x, keep_prob) if drop else x
195 return output
70@hparams(default=0.0, required=False)
71def dropout(self):
72 pass
62def Dropout(x, rate, training) :
63 return tf.layers.dropout(inputs=x, rate=rate, training=training)
346def test_dropout():
347 """Tests whether dropout layer reads in probability correctly"""
348 rnn = RNN(layers_info=[["lstm", 20], ["gru", 10], ["linear", 20], ["linear", 1]],
349 hidden_activations="relu", output_activation="sigmoid", dropout=0.9999,
350 initialiser="xavier")
351 assert rnn.dropout_layer.rate == 0.9999
352 assert not solves_simple_problem(X, y, rnn)
353 rnn = RNN(layers_info=[["lstm", 20], ["gru", 10], ["linear", 20], ["linear", 1]],
354 hidden_activations="relu", output_activation=None, dropout=0.0000001,
355 initialiser="xavier")
356 assert rnn.dropout_layer.rate == 0.0000001
357 assert solves_simple_problem(X, y, rnn)

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