Issue 1:
Thank you for the amazing work.

I am using Tensorboard to visualize my training, and it works just fine.

But when I need to change the frequency of loss scalers, I can't find api to do this. I want to more scalers log, maybe log end of every batch. Now I can only get one log during an epoch. Thanks a lot.

tensorboard_callback = TensorBoard(log_dir = log_dir,
histogram_freq = 1,
write_graph = True,
write_images = False,
embeddings_freq = embeddings_freq,
embeddings_layer_names = None,
embeddings_metadata = "w2v_metadata.tsv")

[x ] Check that you are up-to-date with the master branch of Keras. You can update with:

pip install git+git://github.com/fchollet/keras.git --upgrade --no-deps

[x ] If running on TensorFlow, check that you are up-to-date with the latest version. The installation instructions can be found here.

If running on Theano, check that you are up-to-date with the master branch of Theano. You can update with:

pip install git+git://github.com/Theano/Theano.git --upgrade --no-deps

Provide a link to a GitHub Gist of a Python script that can reproduce your issue (or just copy the script here if it is short).

Issue 2:
Please make sure that the boxes below are checked before you submit your issue. If your issue is an implementation question, please ask your question on StackOverflow or join the Keras Slack channel and ask there instead of filing a GitHub issue.

Thank you!

Check that you are up-to-date with the master branch of Keras. You can update with:

pip install git+git://github.com/fchollet/keras.git --upgrade --no-deps

If running on Theano, check that you are up-to-date with the master branch of Theano. You can update with:

pip install git+git://github.com/Theano/Theano.git --upgrade --no-deps

Provide a link to a GitHub Gist of a Python script that can reproduce your issue (or just copy the script here if it is short).

from keras.layers.wrappers import Bidirectional
from keras.layers import LSTM, Dense, Input
from keras.models import Model
import numpy as np
data=np.random.rand(1,5,10)
label=np.asarray([[1,0,0]])

inp = Input(shape=data.shape[1:])
x = Bidirectional(LSTM(units=32,recurrent_dropout=0.5))(inp)
x = Dense(3,activation='softmax')(x)
model = Model(input=inp, output=x)
model.compile(loss='binary_crossentropy',
optimizer='adam',
metrics=['accuracy'])
model.fit(data, label, epochs=1)

Above code gives the following error, If I remove recurrent_dropout=0.5 it trains without error:

MissingInputError: Input 0 of the graph (indices start from 0), used to compute if{}(keras_learning_phase, Elemwise{true_div,no_inplace}.0, Reshape{2}.0), was not provided and not given a value. Use the Theano flag exception_verbosity='high', for more information on this error.

Backtrace when that variable is created:

File "<ipython-input-1-aa27b7e1da0a>", line 1, in <module>
runfile('/home/bozkurt.a/DEJ/train_lstm_seq.py', wdir='/home/bozkurt.a/DEJ')
File "/home/bozkurt.a/miniconda2/lib/python2.7/site-packages/spyder/utils/site/sitecustomize.py", line 866, in runfile
execfile(filename, namespace)
File "/home/bozkurt.a/miniconda2/lib/python2.7/site-packages/spyder/utils/site/sitecustomize.py", line 94, in execfile
builtins.execfile(filename, *where)
File "/home/bozkurt.a/DEJ/train_lstm_seq.py", line 11, in <module>
from keras.models import Model
File "/home/bozkurt.a/miniconda2/lib/python2.7/site-packages/Keras-2.0.2-py2.7.egg/keras/__init__.py", line 3, in <module>
from . import activations
File "/home/bozkurt.a/miniconda2/lib/python2.7/site-packages/Keras-2.0.2-py2.7.egg/keras/activations.py", line 3, in <module>
from . import backend as K
File "/home/bozkurt.a/miniconda2/lib/python2.7/site-packages/Keras-2.0.2-py2.7.egg/keras/backend/__init__.py", line 61, in <module>
from .theano_backend import *
File "/home/bozkurt.a/miniconda2/lib/python2.7/site-packages/Keras-2.0.2-py2.7.egg/keras/backend/theano_backend.py", line 28, in <module>
_LEARNING_PHASE = T.scalar(dtype='uint8', name='keras_learning_phase') # 0 = test, 1 = train