Dropout - 1 vs 6¶
- Dropout1 → Dropout6 +0 -2
Dropout1 → Dropout6
RENAMED
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@@ -1 +1 @@
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1
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Dropout takes one input data (Tensor<float>) and produces two Tensor outputs,
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2
2
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output (Tensor<float>) and mask (Tensor<bool>). Depending on whether it is in
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3
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test mode or not, the output Y will either be a random dropout, or a simple
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copy of the input. Note that our implementation of Dropout does scaling in
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5
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the training phase, so during testing nothing needs to be done.
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**Attributes**
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7
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-
* **consumed_inputs**:
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8
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-
legacy optimization attribute.
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* **is_test**:
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8
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(int, default 0) if nonzero, run dropout in test mode where the
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output is simply Y = X.
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* **ratio**:
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(float, default 0.5) the ratio of random dropout
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**Inputs**
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13
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* **data** (heterogeneous) - **T**:
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The input data as Tensor.
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**Outputs**
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Between 1 and 2 outputs.
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* **output** (heterogeneous) - **T**:
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The output.
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* **mask** (optional, heterogeneous) - **T**:
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The output mask. If is_test is nonzero, this output is not filled.
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**Type Constraints**
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* **T** in (
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tensor(double),
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tensor(float),
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tensor(float16)
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):
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Constrain input and output types to float tensors.
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