Invalid Argument Error when using Tensorboard callback - tensorflow

Question

I have used a Tensorboard callback in fitting a model consisting of one embedding layer and one SimpleRNN layer. The model performs binary sentiment classification for 9600 input text sequences. They have been tokenised and padded in advance.
# 1. Remove previous logs
!rm -rf ./logs/
# 2. Change to Py_file_dir
os.chdir(...)
# input_dim = 43489 (size of tokenizer word dictionary); output_dim = 100 (GloVe 100d embeddings); input_length = 1403 (length of longest text sequence).
# xtr_pad is padded, tokenised text sequences. nrow = 9600, ncol = input_length = 1403.
model= Sequential()
model.add(Embedding(input_dim, output_dim, input_length= input_length,
weights= [Embedding_matrix], trainable= False))
model.add(SimpleRNN(200))
model.add(Dense(1, activation= 'sigmoid'))
model.compile(loss='binary_crossentropy', optimizer= 'adam', metrics=['accuracy'])
tb = TensorBoard(histogram_freq=1, log_dir= 'tbcallback_prac')
tr_results= model.fit(xtr_pad, ytr, epochs= 2, batch_size= 64, verbose= 1,
validation_split= 0.2, callbacks= [tb])
# In command prompt enter: tensorboard --logdir tbcallback_prac
I have run this on Jupyterlab and on the first time the model trains without issue. I was able to view the Tensorboard statistics on local host.
However when I run this same code a second time, i.e. removing logs and fitting model it completed the first epoch of training, but returns this error before the 2nd epoch begins.
Train on 7680 samples, validate on 1920 samples
Epoch 1/2
7680/7680 [==============================] - ETA: 0s - loss: 0.2919 - accuracy: 0.9004
---------------------------------------------------------------------------
InvalidArgumentError Traceback (most recent call last)
<ipython-input-12-a1cde9b5b1f4> in <module>()
7 tb = TensorBoard(histogram_freq=1, log_dir= 'tbcallback_prac')
8 tr_results= model.fit(xtr_pad, ytr, epochs= 2, batch_size= 64, verbose= 1,
----> 9 validation_split= 0.2, callbacks= [tb])
...
InvalidArgumentError: You must feed a value for placeholder tensor 'embedding_input' with dtype float and shape [?,1403]
[[{{node embedding_input}}]]
Note 1403 is the length of all padded, tokenised sequences in training input 'xtr'.
Thanks in advance for any help!

Answer 1

I have no issue but I think that is a dimensions problem when working on logtis and sigmoid
Layer (type) Output Shape Param #
=================================================================
embedding (Embedding) (None, 3072, 64) 64000
simple_rnn (SimpleRNN) (None, 200) 53000
dense (Dense) (None, 1) 201
=================================================================
Total params: 117,201
Trainable params: 117,201
Non-trainable params: 0
_________________________________________________________________
val_dir: F:\models\checkpoint\ale_highscores_3\validation
Epoch 1/1500
2/2 [==============================] - ETA: 0s - loss: -0.5579 - accuracy: 0.1000[<KerasTensor: shape=(None, 3072) dtype=float32 (created by layer 'embedding_input')>]
<keras.engine.functional.Functional object at 0x00000233003A8550>
Press AnyKey!
2/2 [==============================] - 14s 7s/step - loss: -0.5579 - accuracy: 0.1000 - val_loss: -0.6446 - val_accuracy: 0.1000
Epoch 2/1500
2/2 [==============================] - ETA: 0s - loss: -0.6588 - accuracy: 0.1000[<KerasTensor: shape=(None, 3072) dtype=float32 (created by layer 'embedding_input')>]
<keras.engine.functional.Functional object at 0x00000233003A8C40>
Press AnyKey!
2/2 [==============================] - 13s 7s/step - loss: -0.6588 - accuracy: 0.1000 - val_loss: -0.7242 - val_accuracy: 0.1000
Epoch 3/1500
1/2 [==============>...............] - ETA: 6s - loss: -0.1867 - accuracy: 0.1429