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fix bug for tf.concat api #22

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fix bug for tf.concat api #22

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4 changes: 2 additions & 2 deletions es_distributed/policies.py
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Original file line number Diff line number Diff line change
Expand Up @@ -181,10 +181,10 @@ def _make_net(self, o):
aidx_na = bins(x, adim, len(acvals_k), 'out') # values in [0, k-1]
a = tf.gather_nd(
acvals_ak,
tf.concat(2, [
tf.concat([
tf.tile(np.arange(adim)[None, :, None], [tf.shape(aidx_na)[0], 1, 1]),
tf.expand_dims(aidx_na, -1)
]) # (n,a,2)
], 2) # (n,a,2)
) # (n,a)
elif ac_bin_mode == 'continuous':
a = U.dense(x, adim, 'out', U.normc_initializer(0.01))
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8 changes: 4 additions & 4 deletions es_distributed/tf_util.py
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Original file line number Diff line number Diff line change
Expand Up @@ -28,7 +28,7 @@ def max(x, axis=None, keepdims=False):
def min(x, axis=None, keepdims=False):
return tf.reduce_min(x, reduction_indices=None if axis is None else [axis], keep_dims = keepdims)
def concatenate(arrs, axis=0):
return tf.concat(axis, arrs)
return tf.concat(arrs, axis)
def argmax(x, axis=None):
return tf.argmax(x, dimension=axis)

Expand Down Expand Up @@ -179,8 +179,8 @@ def intprod(x):

def flatgrad(loss, var_list):
grads = tf.gradients(loss, var_list)
return tf.concat(0, [tf.reshape(grad, [numel(v)])
for (v, grad) in zip(var_list, grads)])
return tf.concat([tf.reshape(grad, [numel(v)])
for (v, grad) in zip(var_list, grads)], 0)

class SetFromFlat(object):
def __init__(self, var_list, dtype=tf.float32):
Expand All @@ -202,7 +202,7 @@ def __call__(self, theta):

class GetFlat(object):
def __init__(self, var_list):
self.op = tf.concat(0, [tf.reshape(v, [numel(v)]) for v in var_list])
self.op = tf.concat([tf.reshape(v, [numel(v)]) for v in var_list], 0)
def __call__(self):
return get_session().run(self.op)

Expand Down