lstm network builders using tf lstm

baselines/common/models.py

@@ -92,48 +92,6 @@ def lstm(nlstm=128, layer_norm=False):
 
     return network_fn
 
-def tflstm_static(nlstm=128, layer_norm=False):
-    def network_fn(X, nenv=1):
-        nbatch = X.shape[0] 
-        nsteps = nbatch // nenv
-         
-        h = tf.layers.flatten(X)
-        rnn_cell = tf.nn.rnn_cell.BasicLSTMCell(nlstm, state_is_tuple=False, forget_bias=0.0)
-
-        S = tf.placeholder(tf.float32, rnn_cell.zero_state(nenv, dtype=tf.float32).shape) #states
-        M = tf.placeholder(tf.float32, [nbatch]) #mask (done t-1)
-
-        xs = batch_to_seq(h, nenv, nsteps)
-
-        h5, snew = tf.nn.static_rnn(rnn_cell, xs, initial_state=S)
-           
-        h = seq_to_batch(h5)
-
-        initial_state = np.zeros(S.shape.as_list(), dtype=float)
-
-        return h, {'S':S, 'M':M, 'state':snew, 'initial_state':initial_state}
-
-    return network_fn
-
-def tflstm(nlstm=128):
-    def network_fn(X, nenv=1):
-        nbatch = X.shape[0] 
-        nsteps = nbatch // nenv
-         
-        h = tf.layers.flatten(X)
-        rnn_cell = tf.nn.rnn_cell.BasicLSTMCell(nlstm, state_is_tuple=False, forget_bias=0.0)
-
-        S = tf.placeholder(tf.float32, rnn_cell.zero_state(nenv, dtype=tf.float32).shape) #states
-        M = tf.placeholder(tf.float32, [nbatch]) #mask (done t-1)
-        initial_state = np.zeros(S.shape)
-
-        h = tf.reshape(h, (-1, nsteps, h.shape[-1]))
-        h, snew = tf.nn.dynamic_rnn(rnn_cell, h, initial_state=S)     
-
-        h = tf.reshape(h, (-1, h.shape[-1]))
-        return h, {'S':S, 'M':M, 'state':snew, 'initial_state':initial_state}
-
-    return network_fn
 
 def cnn_lstm(nlstm=128, layer_norm=False, **conv_kwargs):
     def network_fn(X, nenv=1):
@@ -211,10 +169,6 @@ def get_network_builder(name):
         return mlp
     elif name == 'lstm':
         return lstm
-    elif name == 'tflstm_static':
-        return tflstm_static
-    elif name == 'tflstm':
-        return tflstm
     elif name == 'cnn_lstm':
         return cnn_lstm
     elif name == 'cnn_lnlstm':