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CNN_aux.py
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CNN_aux.py
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#%% CRPS and CNNs
#%%
import tensorflow as tf
import numpy as np
from tensorflow import keras
from tensorflow.keras import models,layers
import tensorflow_probability as tfp
tfd = tfp.distributions
import math
import xarray as xr
from scipy.stats import pearsonr,gamma
import os
import gc
import random
# %% CRPS of CSG distribution , following Grönquist et al. (2021)
class loss_CRPS_CSGD_V2(keras.losses.Loss):
def __init__(self ): #, name="loss_CRPS_CSGD"
super().__init__()
def call(self, obs, par_mat):
shift = par_mat[:,0] - 1e-6 #negative value
mu = par_mat[:,1]
sigma = par_mat[:,2]
shape = (mu/sigma)**2
scale = (sigma**2)/mu
rate = 1/scale
betaf = tf.exp ( tfp.math.lbeta(0.5, (shape)+0.5) )
dist_k = tfd.Gamma(shape, rate )
dist_kp1 = tfd.Gamma(shape+1 , rate )
dist_2k = tfd.Gamma(shape*2 , rate )
Fyk = dist_k.cdf( obs-shift )
Fck = dist_k.cdf( -shift )
FykP1 = dist_kp1.cdf(obs-shift )
FckP1 = dist_kp1.cdf( -shift )
F2c2k = dist_2k.cdf( 2*(-shift) )
crps_scaled = (obs-shift)*(2.*Fyk-1.) + shift*(Fck**2) + shape*scale*(1.+2.*Fck*FckP1-(Fck**2)-2*FykP1) \
- (shape*scale/(math.pi))*betaf*(1.-F2c2k)
ind_notnan = tf.where( ~tf.math.is_nan(crps_scaled) ) #I add this to avoid use NaN CRPS values
CRPS_notnan = ( tf.reduce_mean( tf.gather_nd(crps_scaled,ind_notnan) ) )
return CRPS_notnan
#get an object
loss_CRPS_CSGD_object_V2 = loss_CRPS_CSGD_V2()
#%%
def ANN3out_2emb(image_size, n_channel, n_word_max_lat,n_word_max_lon, n_out_emb_dim, apply_dropout=False):
#conv kernel size, conv channel, input words dimension, embedding output dimension
##lat, lon embedding separately
n_feature = [ 64, 32, 16 ]
initializer = tf.random_normal_initializer( 0, 0.01)
lat_in = layers.Input(shape=(1,))#lat embed
x1 = layers.Embedding(input_dim=n_word_max_lat, output_dim = n_out_emb_dim, input_length=1 )(lat_in)
lat_emb = layers.Flatten()(x1)
lon_in = layers.Input(shape=(1,))#lon embed
x2 = layers.Embedding(input_dim=n_word_max_lon, output_dim = n_out_emb_dim, input_length=1 )(lon_in)
lon_emb = layers.Flatten()(x2)
#----------------
#image input:
features_in = layers.Input( shape=( image_size,image_size,n_channel) )
features_in_vec = layers.Flatten()(features_in) #flatten convolution results
#combine
input_combine = layers.Concatenate()([features_in_vec, lat_emb, lon_emb] )
x = layers.Dense(n_feature[0], kernel_initializer=initializer)( input_combine)
x = layers.BatchNormalization()(x)
x = layers.Dense(n_feature[1], kernel_initializer=initializer)( x)
x = layers.BatchNormalization()(x)
x = layers.Dense(n_feature[2], kernel_initializer=initializer)( x)
x = layers.BatchNormalization()(x)
if (apply_dropout):
x =layers.Dropout(0.5)(x)
#OUTPUT LAYER
x = layers.Dense(3, kernel_initializer=initializer)(x)
#multi inputs
model = keras.Model([lat_in, lon_in, features_in], x )
return model
#%%
def lenet3out_1conv_2emb(image_size, n_channel, n_word_max_lat,n_word_max_lon, n_out_emb_dim, apply_dropout=False):
#conv kernel size, conv channel, input words dimension, embedding output dimension
##lat, lon embedding separately
initializer = tf.random_normal_initializer( 0, 0.01)
lat_in = layers.Input(shape=(1,))#lat embed
x1 = layers.Embedding(input_dim=n_word_max_lat, output_dim = n_out_emb_dim, input_length=1 )(lat_in)
lat_emb = layers.Flatten()(x1)
lon_in = layers.Input(shape=(1,))#lon embed
x2 = layers.Embedding(input_dim=n_word_max_lon, output_dim = n_out_emb_dim, input_length=1 )(lon_in)
lon_emb = layers.Flatten()(x2)
#----------------
#image input:
f = [ 64 ]
kernel_size = 3
features_in = layers.Input( shape=( image_size,image_size,n_channel) )
# Conv => ReLu => Pool
x = layers.Conv2D( filters=f[0], kernel_size=kernel_size, strides=1, activation='elu',padding='valid', kernel_initializer=initializer)(features_in)
# res.add(layers.MaxPooling2D( pool_size=(2,2), strides=2, padding='valid'))
x = layers.BatchNormalization()(x)
if (apply_dropout):
x =layers.Dropout(0.5)(x)
x = layers.Flatten()(x) #flatten convolution results
#combine
input_combine = layers.Concatenate()([x, lat_emb, lon_emb] )
#Dense 1
x = layers.Dense(6, kernel_initializer=initializer)( input_combine)
#Dense 2
x = layers.Dense(3, kernel_initializer=initializer)( x)
#multi inputs
model = keras.Model([lat_in, lon_in, features_in], x )
return model
#%%
def lenet3out_2conv_2emb(image_size, n_channel, n_word_max_lat,n_word_max_lon, n_out_emb_dim, apply_dropout=False):
#conv kernel size, conv channel, input words dimension, embedding output dimension
##lat, lon embedding separately
initializer = tf.random_normal_initializer( 0, 0.01)
lat_in = layers.Input(shape=(1,))#lat embed
x1 = layers.Embedding(input_dim=n_word_max_lat, output_dim = n_out_emb_dim, input_length=1 )(lat_in)
lat_emb = layers.Flatten()(x1)
lon_in = layers.Input(shape=(1,))#lon embed
x2 = layers.Embedding(input_dim=n_word_max_lon, output_dim = n_out_emb_dim, input_length=1 )(lon_in)
lon_emb = layers.Flatten()(x2)
#----------------
#image input:
# f = [ 8, 4 ]
# f = [ 32, 16 ]
f = [ 64, 32 ]
kernel_size = 3
features_in = layers.Input( shape=( image_size,image_size,n_channel) )
# Conv => ReLu
x = layers.Conv2D( filters=f[0], kernel_size=kernel_size, strides=1, activation='elu',padding='valid', kernel_initializer=initializer)(features_in)
x = layers.BatchNormalization()(x)
# Conv => ReLu
x = layers.Conv2D( filters=f[1], kernel_size=kernel_size, strides=1, activation='elu',padding='valid', kernel_initializer=initializer)(x)
x = layers.BatchNormalization()(x)
if (apply_dropout):
x =layers.Dropout(0.5)(x)
x = layers.Flatten()(x) #flatten convolution results
#combine
input_combine = layers.Concatenate()([x, lat_emb, lon_emb] )
x = layers.Dense(6, kernel_initializer=initializer)( input_combine)
#Dense 2
x = layers.Dense(3, kernel_initializer=initializer)( x)
#multi inputs
model = keras.Model([lat_in, lon_in, features_in], x )
return model
#%%
def lenet3out_3conv_2emb(image_size, n_channel, n_word_max_lat,n_word_max_lon, n_out_emb_dim, apply_dropout=False):
#conv kernel size, conv channel, input words dimension, embedding output dimension
##lat, lon embedding separately
initializer = tf.random_normal_initializer( 0, 0.01)
lat_in = layers.Input(shape=(1,))#lat embed
x1 = layers.Embedding(input_dim=n_word_max_lat, output_dim = n_out_emb_dim, input_length=1 )(lat_in)
lat_emb = layers.Flatten()(x1)
lon_in = layers.Input(shape=(1,))#lon embed
x2 = layers.Embedding(input_dim=n_word_max_lon, output_dim = n_out_emb_dim, input_length=1 )(lon_in)
lon_emb = layers.Flatten()(x2)
#----------------
#image input:
f = [ 64, 32, 16 ]
kernel_size = 3
features_in = layers.Input( shape=( image_size,image_size,n_channel) )
# Conv => ReLu
x = layers.Conv2D( filters=f[0], kernel_size=kernel_size, strides=1, activation='elu',padding='valid', kernel_initializer=initializer)(features_in)
x = layers.BatchNormalization()(x)
# Conv => ReLu
x = layers.Conv2D( filters=f[1], kernel_size=kernel_size, strides=1, activation='elu',padding='valid', kernel_initializer=initializer)(x)
x = layers.BatchNormalization()(x)
# Conv => ReLu
x = layers.Conv2D( filters=f[2], kernel_size=kernel_size, strides=1, activation='elu',padding='valid', kernel_initializer=initializer)(x)
x = layers.BatchNormalization()(x)
if (apply_dropout):
x =layers.Dropout(0.5)(x)
x = layers.Flatten()(x) #flatten convolution results
#combine
input_combine = layers.Concatenate()([x, lat_emb, lon_emb] )
x = layers.Dense(6, kernel_initializer=initializer)( input_combine)
#Dense 2
x = layers.Dense(3, kernel_initializer=initializer)( x)
#multi inputs
model = keras.Model([lat_in, lon_in, features_in], x )
return model
#%%
def lenet3out_4conv_2emb(image_size, n_channel, n_word_max_lat,n_word_max_lon, n_out_emb_dim, apply_dropout=False):
#conv kernel size, conv channel, input words dimension, embedding output dimension
##lat, lon embedding separately
initializer = tf.random_normal_initializer( 0, 0.01)
lat_in = layers.Input(shape=(1,))#lat embed
x1 = layers.Embedding(input_dim=n_word_max_lat, output_dim = n_out_emb_dim, input_length=1 )(lat_in)
lat_emb = layers.Flatten()(x1)
lon_in = layers.Input(shape=(1,))#lon embed
x2 = layers.Embedding(input_dim=n_word_max_lon, output_dim = n_out_emb_dim, input_length=1 )(lon_in)
lon_emb = layers.Flatten()(x2)
#----------------
#image input:
# f = [ 8, 4 ]
# f = [ 32, 16 ]
f = [ 64, 32, 16, 8 ]
kernel_size = 3
features_in = layers.Input( shape=( image_size,image_size,n_channel) )
# Conv => ReLu
x = layers.Conv2D( filters=f[0], kernel_size=kernel_size, strides=1, activation='elu',padding='valid', kernel_initializer=initializer)(features_in)
x = layers.BatchNormalization()(x)
# Conv => ReLu
x = layers.Conv2D( filters=f[1], kernel_size=kernel_size, strides=1, activation='elu',padding='valid', kernel_initializer=initializer)(x)
x = layers.BatchNormalization()(x)
# Conv => ReLu
x = layers.Conv2D( filters=f[2], kernel_size=kernel_size, strides=1, activation='elu',padding='valid', kernel_initializer=initializer)(x)
x = layers.BatchNormalization()(x)
# Conv => ReLu
x = layers.Conv2D( filters=f[3], kernel_size=kernel_size, strides=1, activation='elu',padding='valid', kernel_initializer=initializer)(x)
x = layers.BatchNormalization()(x)
if (apply_dropout):
x =layers.Dropout(0.5)(x)
x = layers.Flatten()(x) #flatten convolution results
#combine
input_combine = layers.Concatenate()([x, lat_emb, lon_emb] )
x = layers.Dense(6, kernel_initializer=initializer)( input_combine)
#Dense 2
x = layers.Dense(3, kernel_initializer=initializer)( x)
#multi inputs
model = keras.Model([lat_in, lon_in, features_in], x )
return model