MNIST 2c2d¶
-
class
deepobs.tensorflow.testproblems.mnist_2c2d.
mnist_2c2d
(batch_size, weight_decay=None)[source]¶ DeepOBS test problem class for a two convolutional and two dense layered neural network on MNIST.
The network has been adapted from the TensorFlow tutorial and consists of
- two conv layers with ReLUs, each followed by max-pooling
- one fully-connected layers with ReLUs
- 10-unit output layer with softmax
- cross-entropy loss
- No regularization
The weight matrices are initialized with truncated normal (standard deviation of
0.05
) and the biases are initialized to0.05
.Parameters: - batch_size (int) -- Batch size to use.
- weight_decay (float) -- No weight decay (L2-regularization) is used in this
test problem. Defaults to
None
and any input here is ignored.
-
dataset
¶ The DeepOBS data set class for MNIST.
-
train_init_op
¶ A tensorflow operation initializing the test problem for the training phase.
-
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.
-
regularizer
¶ A scalar tf.Tensor containing a regularization term. Will always be
0.0
since no regularizer is used.
-
accuracy
¶ A scalar tf.Tensor containing the mini-batch mean accuracy.