tf.concat arguments reordered in time_distributed and some examples (t…

…flearn#710)

examples/basics/weights_loading_scope.py

@@ -103,7 +103,7 @@ def __init__(self):
         with tf.variable_scope("scope2") as scope:
             net_dnn = Model2.make_core_network(inputs)	# shape (?, 10)
 
-        network = tf.concat(1, [net_conv, net_dnn], name="concat")	# shape (?, 20)
+        network = tf.concat([net_conv, net_dnn], 1, name="concat")	# shape (?, 20)
         network = tflearn.fully_connected(network, 10, activation="softmax")
         network = regression(network, optimizer='adam', learning_rate=0.01,
                              loss='categorical_crossentropy', name='target')

examples/others/recommender_wide_and_deep.py

@@ -239,7 +239,7 @@ def deep_model(self, wide_inputs, n_inputs, n_nodes=[100, 50], use_dropout=False
                 print ("    %s_embed = %s" % (cc, cc_embed_var[cc]))
             flat_vars.append(tf.squeeze(cc_embed_var[cc], squeeze_dims=[1], name="%s_squeeze" % cc))
 
-        network = tf.concat(1, [wide_inputs] + flat_vars, name="deep_concat")
+        network = tf.concat([wide_inputs] + flat_vars, 1, name="deep_concat")
         for k in range(len(n_nodes)):
             network = tflearn.fully_connected(network, n_nodes[k], activation="relu", name="deep_fc%d" % (k+1))
             if use_dropout:

tflearn/layers/core.py

@@ -644,4 +644,4 @@ def time_distributed(incoming, fn, args=None, scope=None):
       x = map(lambda t: tf.reshape(t, [-1, 1]+utils.get_incoming_shape(t)[1:]), x)
     except:
       x = list(map(lambda t: tf.reshape(t, [-1, 1]+utils.get_incoming_shape(t)[1:]), x))
-    return tf.concat(1, x)
+    return tf.concat(x, 1)