import tensorflow.keras as keras
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten, Activation
# Step 1: Define and compile model
model.add(Dense(10, activation='sigmoid', input_shape=((30,))))
model.add(Dense(20, activation='sigmoid'))
model.add(Dense(10, activation='sigmoid'))
model.add(Dense(1, activation='sigmoid'))
model.compile(optimizer=keras.optimizers.Adam(),
loss=keras.losses.mean_squared_logarithmic_error)
# Step 2: Fit model and log callbacks
params = {'batch_size': 30,
callbacked = model.fit(X_train, y_train,
batch_size=params['batch_size'],
verbose=params['verbose'],
validation_data=(X_test, y_test),
# log to Neptune using NeptuneCallback
callbacks=[NeptuneCallback(run=run)]
print('Step 2.2: Training callbacks successfully logged!')