def main():
tf.set_random_seed(<NUM_LIT>)<EOL>input_width = <NUM_LIT:32><EOL>input_channel = <NUM_LIT:3><EOL>inputs = tf.Variable(tf.random_uniform((<NUM_LIT:1>, input_width, input_channel)), name='<STR_LIT:input>')<EOL>inputs = tf.identity(inputs, "<STR_LIT>")<EOL>filter_width = <NUM_LIT:4><EOL>output_channels = <NUM_LIT:6><EOL>filters = tf.Variable(tf.random_uniform((filter_width, input_channel, output_channels)))<EOL>conv_out = tf.nn.conv1d(inputs, filters, stride=<NUM_LIT:1>, padding="<STR_LIT>")<EOL>bias = tf.Variable(tf.zeros([output_channels]))<EOL>output = tf.nn.tanh(tf.nn.bias_add(conv_out, bias), name="<STR_LIT>")<EOL>net_outputs = map(lambda x: tf.get_default_graph().get_tensor_by_name(x), argv[<NUM_LIT:2>].split('<STR_LIT:U+002C>'))<EOL>run_model(net_outputs, argv[<NUM_LIT:1>], backward=(argv[<NUM_LIT:3>] == '<STR_LIT:True>'))<EOL>
You can also run these commands manually to generate the pb file
1. git clone https://github.com/tensorflow/models.git
2. export PYTHONPATH=Path_to_your_model_folder
3. python temporal_convolution.py