# 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',
# 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)
# 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',