# This version uses only a small quantity of training data
import os
import tensorflow as tf
import ML_Utilities
import pickle
# How many epochs to train for
n_epochs=100
# How many fields to train on
n_fields=100
# Create TensorFlow Dataset object from the prepared training data
(tr_data,n_steps) = ML_Utilities.dataset(purpose='training',
source='20CR2c',
variable='prmsl',
length=n_fields)
tr_data = tr_data.repeat(n_epochs)
# Need to reshape the data to linear, and produce a tuple
# (source,target) for model
def to_model(ict):
ict=tf.reshape(ict,[1,91*180])
return(ict,ict)
tr_data = tr_data.map(to_model)
# Similar dataset from the prepared test data
(tr_test,test_steps) = ML_Utilities.dataset(purpose='test',
source='20CR2c',
variable='prmsl')
tr_test = tr_test.repeat(n_epochs)
parser.add_argument("--member",
help="Ensemble member",
default=1,
type=int,
required=False)
args = parser.parse_args()
# Get the 20CR data
ic = twcr.load('prmsl',
datetime.datetime(args.year, args.month, args.day, args.hour),
version='2c')
ic = ic.extract(iris.Constraint(member=args.member))
# Normalisation - Pa to mean=0, sd=1 - and back
normalise = ML_Utilities.get_normalise_function(source='20CR2c',
variable='prmsl')
unnormalise = ML_Utilities.get_unnormalise_function(source='20CR2c',
variable='prmsl')
# Get the original autoencoder
model_save_file = ("%s/Machine-Learning-experiments/simple_autoencoder/" +
"saved_models/Epoch_%04d") % (os.getenv('SCRATCH'), 100)
autoencoder_original = tf.keras.models.load_model(model_save_file)
# Run the data through the original autoencoder and convert back to iris cube
pm_original = ic.copy()
pm_original.data = normalise(pm_original.data)
ict = tf.convert_to_tensor(pm_original.data, numpy.float32)
ict = tf.reshape(ict, [1, 91 * 180]) # ????
result = autoencoder_original.predict_on_batch(ict)
result = tf.reshape(result, [91, 180])
# is the equator - this limits the problems with boundary conditions.
# This version looks at precip
import os
import tensorflow as tf
import ML_Utilities
import pickle
import numpy
# How many epochs to train for
n_epochs = 50
# Create TensorFlow Dataset object from the prepared training data
(tr_data, n_steps) = ML_Utilities.dataset(purpose='training',
source='rotated_pole/20CR2c',
variable='prate')
tr_data = tr_data.repeat(n_epochs)
# Also produce a tuple (source,target) for model
def to_model(ict):
ict = tf.reshape(ict, [79, 159, 1])
return (ict, ict)
tr_data = tr_data.map(to_model)
tr_data = tr_data.batch(1)
# Similar dataset from the prepared test data
(tr_test, test_steps) = ML_Utilities.dataset(purpose='test',
# Very simple autoencoder for 20CR air.2m fields.
# Single, fully-connected layer as encoder+decoder, 32 neurons.
# Very unlikely to work well at all, but this isn't about good
# results, it's about getting started.
import os
import tensorflow as tf
import ML_Utilities
import pickle
# How many epochs to train for
n_epochs = 100
# Create TensorFlow Dataset object from the prepared training data
(tr_data, n_steps) = ML_Utilities.dataset(purpose='training',
source='20CR2c',
variable='air.2m')
tr_data = tr_data.repeat(n_epochs)
# Need to reshape the data to linear, and produce a tuple
# (source,target) for model
def to_model(ict):
ict = tf.reshape(ict, [1, 18048])
return (ict, ict)
tr_data = tr_data.map(to_model)
# Similar dataset from the prepared test data
(tr_test, test_steps) = ML_Utilities.dataset(purpose='test',