SVHN 3c3d¶
-
class
deepobs.tensorflow.testproblems.svhn_3c3d.
svhn_3c3d
(batch_size, weight_decay=0.002)[source]¶ DeepOBS test problem class for a three convolutional and three dense layered neural network on SVHN.
The network consists of
- thre conv layers with ReLUs, each followed by max-pooling
- two fully-connected layers with
512
and256
units and ReLU activation - 10-unit output layer with softmax
- cross-entropy loss
- L2 regularization on the weights (but not the biases) with a default factor of 0.002
The weight matrices are initialized using Xavier initialization and the biases are initialized to
0.0
.Parameters: - batch_size (int) -- Batch size to use.
- weight_decay (float) -- Weight decay factor. Weight decay (L2-regularization)
is used on the weights but not the biases. Defaults to
0.002
.
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dataset
¶ The DeepOBS data set class for SVHN.
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train_init_op
¶ A tensorflow operation initializing the test problem for the training phase.
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train_eval_init_op
¶ A tensorflow operation initializing the test problem for evaluating on training data.
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test_init_op
¶ A tensorflow operation initializing the test problem for evaluating on test data.
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losses
¶ A tf.Tensor of shape (batch_size, ) containing the per-example loss values.
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regularizer
¶ A scalar tf.Tensor containing a regularization term.
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accuracy
¶ A scalar tf.Tensor containing the mini-batch mean accuracy.