2

With this code I get an error while I try to run the prediction with X_test. The error occurs after the fitting, while y_pred = model.predict(X_test) is executed.

X_train.input_shape()
-> (784, 300, 7)
y_train.input_shape()
-> (784, 300, 1)

X_test.input_shape()
-> (124,300,7)
y_test.input_shape()
-> (124,300,1)


batchsize = 4
model = Sequential()
model.add(Masking(mask_value=0, batch_input_shape=(batchsize, len(X_train[0]),len(X_train[0][0]))))
model.add(Bidirectional(LSTM(200, return_sequences=True, stateful=True)))
model.add(TimeDistributed(Dense(len(classdict))))
model.compile(loss="sparse_categorical_crossentropy",
              optimizer=Adam(0.001),
              metrics=['accuracy'])
model.summary()
model.fit(X_train, y_train, epochs=1, batch_size=batchsize)
y_pred = model.predict(X_test)

Error:

Traceback (most recent call last):
  File "lib/python3.7/site-packages/keras/utils/traceback_utils.py", line 67, in error_handler
    raise e.with_traceback(filtered_tb) from None
  File "lib/python3.7/site-packages/tensorflow/python/eager/execute.py", line 55, in quick_execute
    inputs, attrs, num_outputs)
tensorflow.python.framework.errors_impl.InvalidArgumentError: Graph execution error:

Specified a list with shape [4,1] from a tensor with shape [32,1]
     [[{{node TensorArrayUnstack_1/TensorListFromTensor}}]]
     [[sequential/bidirectional/backward_lstm/PartitionedCall]] [Op:__inference_predict_function_13360]

In my opinion, the error could be related to the selected batchsize. In my research I read that the number of samples has to be divisible by the batchsize for stateful LSTMs. So I searched for the greatest common divisor of the number of samples of X_train and X_test, so GCD(124,784) = 4 = batchsize. However, now this error occurs, I have already tried different batchsizes, but then other errors occur. Has someone a idea/fix for this?

bolli
  • 115
  • 5

1 Answers1

3

You just need to make sure that the number of training samples can be divided evenly by the batch size. Here is a working example:

import tensorflow as tf

X_train = tf.random.normal((784, 300, 7))
y_train = tf.random.normal((784, 300, 1))
X_test = tf.random.normal((124,300,7))

batchsize = 4
model = tf.keras.Sequential()
model.add(tf.keras.layers.Masking(mask_value=0, batch_input_shape=(batchsize, len(X_train[0]),len(X_train[0][0]))))
model.add(tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(200, return_sequences=True, stateful=True)))
model.add(tf.keras.layers.TimeDistributed(tf.keras.layers.Dense(1)))
model.compile(loss="mse",
              optimizer=tf.keras.optimizers.Adam(0.001))
model.summary()
model.fit(X_train, y_train, batch_size=batchsize, epochs=1)

Model: "sequential_5"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 masking_5 (Masking)         (4, 300, 7)               0         
                                                                 
 bidirectional_5 (Bidirectio  (4, 300, 400)            332800    
 nal)                                                            
                                                                 
 time_distributed_5 (TimeDis  (4, 300, 1)              401       
 tributed)                                                       
                                                                 
=================================================================
Total params: 333,201
Trainable params: 333,201
Non-trainable params: 0
_________________________________________________________________
196/196 [==============================] - 47s 131ms/step - loss: 1.0052
<keras.callbacks.History at 0x7fee901f8950>

And also explicitly set the batch size when predicting, since according to the docs if left unspecified, batch_size will default to 32:

X_test = tf.random.normal((124,300,7))
y_pred = model.predict(X_test, batch_size=batchsize) 
print(y_pred.shape)

(124, 300, 1)

Also make sure your batch is the same everywhere.

AloneTogether
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