def get_forward_parameters(vocab_size=4716):
t_vars = tf.trainable_variables()
h1_vars_weight = [var for var in t_vars if 'hidden_1' in var.name and 'weights' in var.name]
h1_vars_biases = [var for var in t_vars if 'hidden_1' in var.name and 'biases' in var.name]
h2_vars_weight = [var for var in t_vars if 'hidden_2' in var.name and 'weights' in var.name]
h2_vars_biases = [var for var in t_vars if 'hidden_2' in var.name and 'biases' in var.name]
o1_vars_weight = [var for var in t_vars if 'output_1' in var.name and 'weights' in var.name]
o1_vars_biases = [var for var in t_vars if 'output_1' in var.name and 'biases' in var.name]
o2_vars_weight = [var for var in t_vars if 'output_2' in var.name and 'weights' in var.name]
o2_vars_biases = [var for var in t_vars if 'output_2' in var.name and 'biases' in var.name]
h1_vars_biases = tf.reshape(h1_vars_biases[0],[1,FLAGS.hidden_size_1])
h2_vars_biases = tf.reshape(h2_vars_biases[0],[1,FLAGS.hidden_size_2])
o1_vars_biases = tf.reshape(o1_vars_biases[0],[1,FLAGS.hidden_size_1])
o2_vars_biases = tf.reshape(o2_vars_biases[0],[1,vocab_size])
vars_1 = tf.concat((h1_vars_weight[0],h1_vars_biases),axis=0)
vars_2 = tf.concat((h2_vars_weight[0],h2_vars_biases),axis=0)
vars_3 = tf.concat((o1_vars_weight[0],o1_vars_biases),axis=0)
vars_4 = tf.concat((o2_vars_weight[0],o2_vars_biases),axis=0)
return [vars_1,vars_2,vars_3,vars_4]
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