@register("<STR_LIT>")<EOL>def conv_only(convs=[(<NUM_LIT:32>, <NUM_LIT:8>, <NUM_LIT:4>), (<NUM_LIT:64>, <NUM_LIT:4>, <NUM_LIT:2>), (<NUM_LIT:64>, <NUM_LIT:3>, <NUM_LIT:1>)], **conv_kwargs):
def network_fn(X):<EOL><INDENT>out = tf.cast(X, tf.float32) / <NUM_LIT><EOL>with tf.variable_scope("<STR_LIT>"):<EOL><INDENT>for num_outputs, kernel_size, stride in convs:<EOL><INDENT>out = layers.convolution2d(out,<EOL>num_outputs=num_outputs,<EOL>kernel_size=kernel_size,<EOL>stride=stride,<EOL>activation_fn=tf.nn.relu,<EOL>**conv_kwargs)<EOL><DEDENT><DEDENT>return out<EOL><DEDENT>return network_fn<EOL>
convolutions-only net

Parameters:
----------

conv:       list of triples (filter_number, filter_size, stride) specifying parameters for each layer.

Returns:

function that takes tensorflow tensor as input and returns the output of the last convolutional layer