Ndtyjky

I have tried to build a CNN 1D but the interpreter says me:

AttributeError: 'ProgbarLogger' object has no attribute 'log_values'

Here is the code snippet:

model = Sequential()

model.add(Conv1D(200, 20, activation='relu', padding='same',input_shape=(1154,1024))

print(model.summary())

model.compile(optimizer='adam',

          loss='sparse_categorical_crossentropy',

          metrics=['accuracy'])

history=model.fit(X, y,batch_size=10, epochs=25,validation_split=0.7)

and this is the error:

Layer (type)                 Output Shape              Param #   

=================================================================

conv1d_1 (Conv1D)            (None, 1154, 200)         4096200   

=================================================================

Total params: 4,096,200

Trainable params: 4,096,200

Non-trainable params: 0

_________________________________________________________________

None



Train on 0 samples, validate on 1 samples

Epoch 1/25

Traceback (most recent call last):

  File "binary_classification.py", line 59, in <module>

    history=model.fit(X, y,batch_size=10, epochs=25,validation_split=0.7)

  File "/home/isabella/.local/lib/python3.6/site-packages/keras/engine/training.py",

line 1039, in fit

    validation_steps=validation_steps)

  File "/home/isabella/.local/lib/python3.6/site-packages/keras/engine/training_arrays.py",

line 217, in fit_loop

    callbacks.on_epoch_end(epoch, epoch_logs)

  File "/home/isabella/.local/lib/python3.6/site-packages/keras/callbacks.py",

line 79, in on_epoch_end

    callback.on_epoch_end(epoch, logs)

  File "/home/isabella/.local/lib/python3.6/site-packages/keras/callbacks.py",

line 338, in on_epoch_end

    self.progbar.update(self.seen, self.log_values)

AttributeError: 'ProgbarLogger' object has no attribute 'log_values'



X shape is :(1,1154,1024),

y shape is :(1,1154, 1 )

If you look carefully you will see this line right before stack trace output:

Train on 0 samples, validate on 1 samples

There is no training data! Why? That's because you have set the validation_split to 0.7 so at first 70% of the data points in X (and y) are put aside for validation and the remaining 30% is used for training. Probably the number of data points in X is less than 4 and therefore its 30% would amount to less than 1 which means zero data points remains for training. Either use more than 4 data points or remove the validation_split argument (or lower it such that at least one sample remains for training).