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custom callbacks

Solved!
traderpedroso
Level 1
custom callbacks

im trying to use custom callback 

callbacks = [
    EarlyStopping(monitor='val_accuracy',
                  min_delta=1e-3,
                  patience=5,
                  mode='max',
                  restore_best_weights=True,
                  verbose=1),
]

 

its running ok!  but o dashboard not showing epochs and the  running state only showing ->  Optimization results will appear as soon as they are available.

 

sharing full code below from Training

 

 

from keras.callbacks import EarlyStopping


early_stopping_callback = [
    EarlyStopping(monitor='val_accuracy',
                  min_delta=1e-3,
                  patience=5,
                  mode='max',
                  restore_best_weights=True,
                  verbose=1),
]
# A function that builds train and validation sequences.
# You can define your custom data augmentation based on the original train and validation sequences

#   build_train_sequence_with_batch_size        - function that returns train data sequence depending on
#                                                 batch size
#   build_validation_sequence_with_batch_size   - function that returns validation data sequence depending on
#                                                 batch size
def build_sequences(build_train_sequence_with_batch_size, build_validation_sequence_with_batch_size):
    
    batch_size = 64
    
    train_sequence = build_train_sequence_with_batch_size(batch_size)
    validation_sequence = build_validation_sequence_with_batch_size(batch_size)
    
    return train_sequence, validation_sequence


# A function that contains a call to fit a model.

#   model                 - compiled model
#   train_sequence        - train data sequence, returned in build_sequence
#   validation_sequence   - validation data sequence, returned in build_sequence
#   base_callbacks        - a list of Dataiku callbacks, that are not to be removed. User callbacks can be added to this list
def fit_model(model, train_sequence, validation_sequence, base_callbacks):
    
    epochs = 50
    model.fit_generator(train_sequence,
                        epochs=epochs,
                        callbacks=early_stopping_callback,
                        validation_data=(validation_sequence),
                        shuffle=True)

 

and for model Architecture

 

from keras.layers import Input, Dense
from keras.models import Model
from keras import Sequential
from keras.layers import Embedding, Bidirectional, LSTM, Dense
from keras.optimizers import Adam
# Define the keras architecture of your model in 'build_model' and return it. Compilation must be done in 'compile_model'.
#   input_shapes  - dictionary of shapes per input as defined in features handling
#   n_classes - For classification, number of target classes
def build_model(input_shapes, n_classes=None):

    # This input will receive all the preprocessed features
    # sent to 'main'
    input_main = Input(shape=input_shapes["main"], name="main")
    #input_main = Input(shape=(50), name="name_preprocessed")
    num_alphabets=27
    name_length=50
    embedding_dim=256
    x = Sequential()
    x = Embedding(num_alphabets, embedding_dim, input_length=name_length)(input_main)
    x = Bidirectional(LSTM(units=128, recurrent_dropout=0.2, dropout=0.2))(x)
    x = Dense(1, activation="sigmoid")(x)
 

    predictions = Dense(1, activation='sigmoid')(x)

    # The 'inputs' parameter of your model must contain the
    # full list of inputs used in the architecture
    model = Model(inputs=[input_main], outputs=predictions)

    return model

# Compile your model and return it
#   model   - model defined in 'build_model'
def compile_model(model):
    
    # The loss function depends on the type of problem you solve.
    # 'binary_crossentropy' is appropriate for a binary classification.
    model.compile(loss='binary_crossentropy',
                  optimizer=Adam(learning_rate=0.001),
                  metrics=['accuracy'])

    return model

Operating system used: linux ubuntu

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1 Solution
traderpedroso
Level 1
Author

I got the solution for that I didn't know about the base_callbacks is fundamental for saving a model than append a custom callback to base one !

 

from keras.callbacks import EarlyStopping


def build_sequences(build_train_sequence_with_batch_size, build_validation_sequence_with_batch_size):
    
    batch_size = 64
    
    train_sequence = build_train_sequence_with_batch_size(batch_size)
    validation_sequence = build_validation_sequence_with_batch_size(batch_size)
    
    return train_sequence, validation_sequence





def fit_model(model, train_sequence, validation_sequence, base_callbacks):
    epochs = 1


    early_stopping_callback = EarlyStopping(monitor='val_accuracy', min_delta=1e-3, patience=5, mode='max', restore_best_weights=True, verbose=1)

    base_callbacks.append(early_stopping_callback)

    model.fit_generator(train_sequence,
                        epochs=epochs,
                        callbacks=base_callbacks,
                        validation_data=(validation_sequence),
                        shuffle=True)

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2 Replies
traderpedroso
Level 1
Author

I got the solution for that I didn't know about the base_callbacks is fundamental for saving a model than append a custom callback to base one !

 

from keras.callbacks import EarlyStopping


def build_sequences(build_train_sequence_with_batch_size, build_validation_sequence_with_batch_size):
    
    batch_size = 64
    
    train_sequence = build_train_sequence_with_batch_size(batch_size)
    validation_sequence = build_validation_sequence_with_batch_size(batch_size)
    
    return train_sequence, validation_sequence





def fit_model(model, train_sequence, validation_sequence, base_callbacks):
    epochs = 1


    early_stopping_callback = EarlyStopping(monitor='val_accuracy', min_delta=1e-3, patience=5, mode='max', restore_best_weights=True, verbose=1)

    base_callbacks.append(early_stopping_callback)

    model.fit_generator(train_sequence,
                        epochs=epochs,
                        callbacks=base_callbacks,
                        validation_data=(validation_sequence),
                        shuffle=True)
CoreyS
Community Manager
Community Manager

Thank you for sharing your solution with us @traderpedroso!

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