title: keras.callbacks.Tensorboard doesn't work in Tensorflow eager execution
body: <p>I've been trying to visualise a simple model in Tensorboard. The model is build in Tensorflow Eager Execution mode, using keras.</p>
  
  <p>When fitting the model I get the following error:</p>
  
  <pre><code>&gt;&gt;&gt; history = model.fit(x_train, y_train, epochs=1, batch_size=BatchSize, callbacks=[tensorbrd])
  Traceback (most recent call last):
  File "C:\...\tensorflow\python\ops\gen_logging_ops.py", line 322, in histogram_summary
  "HistogramSummary", name, _ctx._post_execution_callbacks, tag, values)
  tensorflow.python.eager.core._FallbackException: This function does not handle the case of the path where all inputs are not already EagerTensors.
  During handling of the above exception, another exception occurred:
  Traceback (most recent call last):
  File "&lt;stdin&gt;", line 1, in &lt;module&gt;
  File "C:\...\tensorflow\python\keras\engine\training.py", line 1348, in fit 
      validation_steps=validation_steps)
  File "C:\...\tensorflow\python\keras\engine\training_eager.py", line 990, in fit_loop 
      callbacks.set_model(callback_model)
  File "C:\...\tensorflow\python\keras\callbacks.py", line 71, in set_model 
      callback.set_model(model)
  File "C:\...\tensorflow\python\keras\callbacks.py", line 781, in set_model 
      tf_summary.histogram('{}_out'.format(layer.name), layer.output)
  File "C:\...\tensorflow\python\summary\summary.py", line 187, in histogram  
      tag=tag, values=values, name=scope)
  File "C:\...\tensorflow\python\ops\gen_logging_ops.py", line 326, in histogram_summary  
      tag, values, name=name, ctx=_ctx)
  File "C:\...\tensorflow\python\ops\gen_logging_ops.py", line 340, in histogram_summary_eager_fallback 
      _attr_T, (values,) = _execute.args_to_matching_eager([values], _ctx, _dtypes.float32)
  File "C:\...\tensorflow\python\eager\execute.py", line 191, in args_to_matching_eager 
      t, dtype, preferred_dtype=default_dtype, ctx=ctx))
  File "C:\...\tensorflow\python\framework\ops.py", line 1094, in internal_convert_to_tensor 
      ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
  File "C:\...\tensorflow\python\framework\constant_op.py", line 217, in _constant_tensor_conversion_function
      return constant(v, dtype=dtype, name=name)
  File "C:\...\tensorflow\python\framework\constant_op.py", line 167, in constant
      t = convert_to_eager_tensor(value, ctx, dtype)
  File "C:\...\tensorflow\python\framework\constant_op.py", line 113, in convert_to_eager_tensor
      return ops.EagerTensor(value, context=handle, device=device, dtype=dtype)
  ValueError: Attempt to convert a value (&lt;DeferredTensor 'None' shape=(?, 5000) dtype=float32&gt;) with an unsupported type (&lt;class 'tensorflow.python.keras.engine.base_layer.DeferredTensor'&gt;) to a Tensor. 
  </code></pre>
  
  <p>This is based on Tensorflow 1.10 code:</p>
  
  <pre><code>import tensorflow as tf
  from tensorflow import keras 
  import matplotlib.pyplot as plt
  import numpy as np
  import os
  import h5py
  tf.enable_eager_execution()
  BatchSize = 256
  model = keras.Sequential([
      keras.layers.Flatten(input_shape=(1000,5)),
  
      keras.layers.Dense(1, activation=tf.nn.relu)
      ])
  
  
  model.compile(optimizer=tf.train.AdamOptimizer(learning_rate=0.01, beta1=0.9, beta2=0.999),
      loss='mean_squared_error',
      metrics=[ 'mean_squared_error'])
  
  tensorbrd = keras.callbacks.TensorBoard(log_dir=SUMMARIES_FOLDER, histogram_freq=1, write_graph=False, write_images=False)
  history = model.fit(x_train, y_train, epochs=1, batch_size=BatchSize, callbacks=[tensorbrd])
  </code></pre>
  
  <p>Am I making a mistake or is Tensorboard not compatible in this way?
  Any help is greatly appreciated!</p>