thesoens/onemodel.py

import tensorflow as tf
from tensorflow import keras

import numpy as np

import os
import sys


from data import data

os.makedirs('./runs', exist_ok=True)
os.makedirs('./imgs', exist_ok=True)

dex=0
if len(sys.argv)>1:
    dex=int(sys.argv[1])


seed=np.random.randint(100000)
x=data(1000)
np.random.seed(12)
X=data(10000)

np.random.seed(seed)



inp=keras.layers.Input(shape=x.shape[1:])
q=inp
q=keras.layers.Dense(5,activation='relu')(q)
q=keras.layers.Dense(5,activation='relu')(q)
q=keras.layers.Dense(1,activation='linear')(q)

model=keras.models.Model(inputs=inp,outputs=q)

model.compile(optimizer='adam',loss='mse')

model.fit(x,np.ones(len(x)),
            epochs=500,
            batch_size=25,
            validation_split=0.2,
            verbose=1,
            shuffle=True,
            callbacks=[keras.callbacks.EarlyStopping(monitor='val_loss',patience=10)])


#Evaluation phase


x=X

p=model.predict(x)
mp=np.mean(p)
d=(p-mp)**2
d=np.sqrt(np.mean(d,axis=-1))

np.savez_compressed(f"runs/{dex}",d=d,x=x,p=p,mp=mp)


sx=[(xx,dd) for xx,dd in zip(x,d)]
sx.sort(key=lambda x:x[1])
print(sx[0],sx[-1])

sx=[xx for xx,dd in sx]
sx=np.array(sx)

from plt import plt


col1=[1.0,0.0,0.0]
col2=[0.0,1.0,0.0]

col1,col2=np.array(col1),np.array(col2)

ln=len(sx)

cols=[col1*(i/ln)+col2*(1-i/ln) for i in range(ln)]



plt.scatter(sx[:,0],sx[:,1],c=cols)

plt.savefig(f"imgs/{dex}.png")

#plt.plot(sx[:,0],sx[:,1],'.')

plt.how()