keras with tensorflow runs fine, until I add callbacks - tensorflow

Question

I'm running a model using Keras and TensorFlow backend. Everything works perfect:
model = Sequential()
model.add(Dense(dim, input_dim=dim, activation='relu'))
model.add(Dense(200, activation='relu'))
model.add(Dense(1, activation='linear'))
model.compile(loss='mse', optimizer='Adam', metrics=['mae'])
history = model.fit(X, Y, epochs=12,
batch_size=100,
validation_split=0.2,
shuffle=True,
verbose=2)
But as soon as I include logger and callbacks so I can log for tensorboard, I get
InvalidArgumentError (see above for traceback): You must feed a value for placeholder tensor 'input_layer_input_2' with dtype float and shape [?,1329]...
Here's my code: (and actually, it worked 1 time, the very first time, then ecer since been getting that error)
model = Sequential()
model.add(Dense(dim, input_dim=dim, activation='relu'))
model.add(Dense(200, activation='relu'))
model.add(Dense(1, activation='linear'))
model.compile(loss='mse', optimizer='Adam', metrics=['mae'])
logger = keras.callbacks.TensorBoard(log_dir='/tf_logs',
write_graph=True,
histogram_freq=1)
history = model.fit(X, Y,
epochs=12,
batch_size=100,
validation_split=0.2,
shuffle=True,
verbose=2,
callbacks=[logger])

Answer 1

A tensorboard callback uses tf.summary.merge_all function in order to collect all tensors for histogram computations. Because of that - your summary is collecting tensors from previous models not cleared from previous model runs. In order to clear these previous models try:
from keras import backend as K
K.clear_session()
model = Sequential()
model.add(Dense(dim, input_dim=dim, activation='relu'))
model.add(Dense(200, activation='relu'))
model.add(Dense(1, activation='linear'))
model.compile(loss='mse', optimizer='Adam', metrics=['mae'])
logger = keras.callbacks.TensorBoard(log_dir='/tf_logs',
write_graph=True,
histogram_freq=1)
history = model.fit(X, Y,
epochs=12,
batch_size=100,
validation_split=0.2,
shuffle=True,
verbose=2,
callbacks=[logger])