You can use CSVLogger callback.
as example:
from keras.callbacks import CSVLogger
csv_logger = CSVLogger('log.csv', append=True, separator=";")
model.fit(X_train, Y_train, callbacks=[csv_logger])
Look at: Keras Callbacks
More Related Contents:
- How can I use a weight matrix created by a trained neural network to make predictions in another file?
- How does Keras calculate the accuracy?
- Keras Sequential model input layer
- How to concatenate two layers in keras?
- What is the role of TimeDistributed layer in Keras?
- How to use fit_generator with multiple inputs
- keras: how to save the training history attribute of the history object
- Can I send callbacks to a KerasClassifier?
- Keras give input to intermediate layer and get final output
- Make a custom loss function in keras
- Keras not training on entire dataset
- What is a `”Python”` layer in caffe?
- Deep-Learning Nan loss reasons
- How to apply gradient clipping in TensorFlow?
- Can Keras with Tensorflow backend be forced to use CPU or GPU at will?
- Negative dimension size caused by subtracting 3 from 1 for ‘conv2d_2/convolution’
- How to get other metrics in Tensorflow 2.0 (not only accuracy)?
- How can I use a pre-trained neural network with grayscale images?
- Removing then Inserting a New Middle Layer in a Keras Model
- Save and load model optimizer state
- confusion matrix error “Classification metrics can’t handle a mix of multilabel-indicator and multiclass targets”
- Keras accuracy does not change
- Where do I call the BatchNormalization function in Keras?
- How does mask_zero in Keras Embedding layer work?
- Tensor is not an element of this graph
- How to add and remove new layers in keras after loading weights?
- Tensorflow One Hot Encoder?
- ‘Tensor’ object has no attribute ‘lower’
- ValueError: Output tensors to a Model must be the output of a TensorFlow `Layer`
- how to implement custom metric in keras?