logger.info("Saved model history: {}".format(model_history_name))
except Exception as exc:
logger.exception(exc)
# Reset model and history
model = None
history = None
logger.info("FINISHED running simulations")
#################################################
# Main functions for each hardware configuration
#################################################
def main():
""" Main function to run training and prediction. """
mod = functional_sony()
run_simulation(mod)
#######################################################
# Running train_model script, Jupyter Notebook config
#######################################################
enable_cloud_log('INFO')
main()
at each convolutional layer, for example.
"""
import logging
from urllib.parse import urljoin
from tensorflow.train import AdamOptimizer
from tensorflow.keras.callbacks import ModelCheckpoint
from tensorflow.keras.datasets import cifar10
from tensorflow.keras.optimizers import Adam
from model01 import simple_sony, full_sony
from model_utils import enable_cloud_log, plot_imgpair, plot_loss
from custom_loss import mean_absolute_error
logger = logging.getLogger(__name__)
enable_cloud_log(level='DEBUG')
# Create checkpoint callback
checkpoint_path = 'checkpoints/cp.ckpt'
cp_callback = ModelCheckpoint(checkpoint_path,
save_weights_only=True,
verbose=1)
# Dataset of 50,000 32x32 color training images,
# labeled over 10 categories, and 10,000 test images.
(X_train, y_train), (X_test, y_test) = cifar10.load_data()
m = 64
#Y_train = X_train
]
for imgtup in imgman:
# Define model
model = model02()
imgname, imgfunc = imgtup
logger.info("Processing: {}".format(imgtup[0]))
model = fit_model(X_train, Y_test, model, checkpoint_dir, imgtup)
Y_pred = model_predict(model, X_test, imgtup)
review_image_output(X_test, Y_pred, Y_test, imgtup, every=10)
model = None
logger.info("FINISHED running simulations")
if __name__ == "__main__":
enable_cloud_log('DEBUG')
fcov = "simulation_cov.pkl"
fmean = "simulation_mean.pkl"
COVM = read_pickle(fcov)
MEANM = read_pickle(fmean)
run_simulation(fcov, fmean)