zoo.examples.ray.parameter_server package

Submodules

zoo.examples.ray.parameter_server.async_parameter_server module

zoo.examples.ray.parameter_server.model module

class zoo.examples.ray.parameter_server.model.SimpleCNN(learning_rate=0.0001)

Bases: object

apply_gradients(gradients)

compute_accuracy(x, y)

compute_gradients(x, y)

compute_update(x, y)

get_weights()

set_weights(variable_names, weights)

zoo.examples.ray.parameter_server.model.bias_variable(shape)

bias_variable generates a bias variable of a given shape.

zoo.examples.ray.parameter_server.model.conv2d(x, W)

conv2d returns a 2d convolution layer with full stride.

zoo.examples.ray.parameter_server.model.deepnn(x)

deepnn builds the graph for a deep net for classifying digits. Args: x: an input tensor with the dimensions (N_examples, 784), where 784 is the number of pixels in a standard MNIST image. Returns: A tuple (y, keep_prob). y is a tensor of shape (N_examples, 10), with values equal to the logits of classifying the digit into one of 10 classes (the digits 0-9). keep_prob is a scalar placeholder for the probability of dropout.

zoo.examples.ray.parameter_server.model.download_mnist_retry(seed=0, max_num_retries=20)

zoo.examples.ray.parameter_server.model.max_pool_2x2(x)

max_pool_2x2 downsamples a feature map by 2X.

zoo.examples.ray.parameter_server.model.weight_variable(shape)

weight_variable generates a weight variable of a given shape.

zoo.examples.ray.parameter_server.sync_parameter_server module

Module contents