Qfyilyi

I'm trying to get TensorBoard working with Keras.
It looks like I'm able to run an initial model with tf (1.12.0) and keras (2.1.6-tf). I have some simple code. Listed below:

%matplotlib inline

from io import StringIO



import numpy as np

import pandas as pd

import tensorflow as tf



csv = StringIO('''a,b,c,y

0,1,2,0

1,2,0,1

0,2,1,0

3,2,1,1

3,1,2,0''')

data = pd.read_csv(csv)



def tb_cb(batch_size):

    # visualize graphs and grandient

    tb = tf.keras.callbacks.TensorBoard(log_dir='/tmp/test/',

                                       histogram_freq=1,

                                       batch_size=batch_size, write_graph=True,

                                       write_grads=True)

    return tb



m = tf.keras.Sequential([

   # going to change 1 in the line below

    tf.keras.layers.Dense(1, activation='relu', input_shape=(3,), name='hidden1'),

    tf.keras.layers.Dense(1, activation='linear', name='output')

])

m.compile(loss='mse', optimizer='adam', metrics=['mae'])

X = data.iloc[:,:3]

y = data.y

hist = m.fit(X, y, epochs=10, verbose=1, callbacks=[tb_cb(10)],

            validation_data=(X,y))

The first time I run this I get TensorBoard output. I have then changed the number of neurons in the hidden layer and re-run the model.

I get the following error:

---------------------------------------------------------------------------

InvalidArgumentError                      Traceback (most recent call last)

<ipython-input-11-4e4fb6f60bf0> in <module>

     15 y = data.y

     16 hist = m.fit(X, y, epochs=10, verbose=1, callbacks=[tb_cb(10)],

---> 17             validation_data=(X,y))



~/.env/364/lib/python3.6/site-packages/tensorflow/python/keras/engine/training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, max_queue_size, workers, use_multiprocessing, **kwargs)

   1637           initial_epoch=initial_epoch,

   1638           steps_per_epoch=steps_per_epoch,

-> 1639           validation_steps=validation_steps)

   1640 

   1641   def evaluate(self,



~/.env/364/lib/python3.6/site-packages/tensorflow/python/keras/engine/training_arrays.py in fit_loop(model, inputs, targets, sample_weights, batch_size, epochs, verbose, callbacks, val_inputs, val_targets, val_sample_weights, shuffle, initial_epoch, steps_per_epoch, validation_steps)

    231                 sample_weights=val_sample_weights,

    232                 batch_size=batch_size,

--> 233                 verbose=0)

    234             if not isinstance(val_outs, list):

    235               val_outs = [val_outs]



~/.env/364/lib/python3.6/site-packages/tensorflow/python/keras/engine/training_arrays.py in test_loop(model, inputs, targets, sample_weights, batch_size, verbose, steps)

    437         ins_batch[i] = ins_batch[i].toarray()

    438 

--> 439       batch_outs = f(ins_batch)

    440 

    441       if isinstance(batch_outs, list):



~/.env/364/lib/python3.6/site-packages/tensorflow/python/keras/backend.py in __call__(self, inputs)

   2984 

   2985     fetched = self._callable_fn(*array_vals,

-> 2986                                 run_metadata=self.run_metadata)

   2987     self._call_fetch_callbacks(fetched[-len(self._fetches):])

   2988     return fetched[:len(self.outputs)]



~/.env/364/lib/python3.6/site-packages/tensorflow/python/client/session.py in __call__(self, *args, **kwargs)

   1437           ret = tf_session.TF_SessionRunCallable(

   1438               self._session._session, self._handle, args, status,

-> 1439               run_metadata_ptr)

   1440         if run_metadata:

   1441           proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)



~/.env/364/lib/python3.6/site-packages/tensorflow/python/framework/errors_impl.py in __exit__(self, type_arg, value_arg, traceback_arg)

    526             None, None,

    527             compat.as_text(c_api.TF_Message(self.status.status)),

--> 528             c_api.TF_GetCode(self.status.status))

    529     # Delete the underlying status object from memory otherwise it stays alive

    530     # as there is a reference to status from this from the traceback due to



InvalidArgumentError: You must feed a value for placeholder tensor 'dense_9_target' with dtype float and shape [?,?]

     [[{{node dense_9_target}} = Placeholder[dtype=DT_FLOAT, shape=[?,?], _device="/job:localhost/replica:0/task:0/device:CPU:0"]()]]