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keras_g3_cae_true-table.py
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keras_g3_cae_true-table.py
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# Keras Geant3 Events to True table convolutional autoencoder
import sys, os
print(os.path.dirname(sys.executable))
import pickle
import time
import os
from sys import platform
import numpy as np
import matplotlib.pyplot as plt
from geant3_parser import Geant3DataFile
from geant3_parser import build_true_answers_train_set
from keras.models import Sequential
from keras.layers import Dense, MaxPooling2D, Conv2D, UpSampling2D, Cropping2D, Input, Conv2DTranspose
file_name = os.path.join('data', 'shower_geant3_new.dat')
def norm_func(e):
return np.float64(np.log(e) / 11)
# file_name = 'sample_data.txt'
data_file = Geant3DataFile(file_name, skip_lines=3)
# split into input (X) and output (y) variables
parse_start = time.time()
print(f"Start preparing events...")
add_real_xy = False
num_events = 200000
inputs, answers, values = build_true_answers_train_set(data_file, num_events, norm_func=norm_func, rnd_shift=((-2, 2), (-2, 2)))
parse_end = time.time()
print(f"Inputs shape original = {np.shape(inputs)}")
print(f"Total events prepare time = {parse_end - parse_start}")
print(f"max hit value = {np.max(inputs)}")
# print(f"max e = {np.max(true_e)}")
inputs = np.reshape(inputs, (len(inputs), 11, 11, 1)) # -1 => autodetermine
answers = np.reshape(answers, (len(answers), 11, 11, 1)) # -1 => autodetermine
# # Pad with 1 row and column of zeroes, so it divides by 2
inputs = np.pad(inputs, ((0,0), (0,1), (0,1), (0,0)), mode='constant', constant_values=0)
answers = np.pad(answers, ((0,0), (0,1), (0,1), (0,0)), mode='constant', constant_values=0)
# print(f"Inputs shape new = {np.shape(inputs)}")
# Prints 11x11 cells event
def print_event(table):
if not len(table):
print("EMPTY TABLE")
return
split_line = ""
for irow, row in enumerate(table):
if irow == 0:
# First row => making title
col_names = "ROW " + " ".join([f"{column_num:<5}" for column_num in range(len(row))])
spaces = int((len(col_names) - len("COLUMNS"))/2)
header = "{0}COLUMNS{0}".format(spaces*" ")
split_line = "-"*len(col_names)
print()
print(header)
print(col_names)
print(split_line)
cells = f"{irow:<4}| " + " ".join([f"{cell[0]:<5.2}" for cell in row])
print(cells)
# Footer
print(split_line)
print_event(inputs[0]*11)
print_event(answers[0]*11)
print("-----------------------------------")
print_event(inputs[1]*11)
print_event(answers[1]*11)
model = Sequential()
model.add(Input(shape=(12, 12, 1)))
model.add(Conv2D(64, kernel_size=(2, 2), activation='relu', kernel_initializer='he_normal'))
model.add(Conv2D(32, kernel_size=(2, 2), activation='relu', kernel_initializer='he_normal'))
model.add(Conv2D(16, kernel_size=(2, 2), activation='relu', kernel_initializer='he_normal'))
model.add(Conv2D(6, kernel_size=(2, 2), activation='relu', kernel_initializer='he_normal'))
model.add(Conv2D(6, kernel_size=(4, 4), activation='relu', kernel_initializer='he_normal'))
model.add(Conv2D(16, kernel_size=(2, 2), activation='relu', kernel_initializer='he_normal'))
model.add(Conv2D(32, kernel_size=(2, 2), activation='relu', kernel_initializer='he_normal'))
model.add(Conv2D(64, kernel_size=(2, 2), activation='relu', kernel_initializer='he_normal'))
#model.add(Conv2DTranspose(6, kernel_size=(2, 2), activation='relu', kernel_initializer='he_normal'))
#model.add(Conv2DTranspose(16, kernel_size=(2, 2), activation='relu', kernel_initializer='he_normal'))
#model.add(Conv2DTranspose(32, kernel_size=(2, 2), activation='relu', kernel_initializer='he_normal'))
#model.add(Conv2DTranspose(64, kernel_size=(2, 2), activation='relu', kernel_initializer='he_normal'))
model.add(Conv2D(1, kernel_size=(2, 2), activation='sigmoid', padding='same'))
model.summary()
'''
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['acc', 'mse', 'mae'])
# output layer
#model.compile(optimizer='adam', loss='mean_squared_error', metrics=['acc', 'mse', 'mae'])
#model.compile(optimizer= 'adam', loss = 'binary_crossentropy')
history = model.fit(inputs, answers, epochs=25, batch_size=32, validation_split=0.2)
# compile the keras model
# model.compile(loss='binary_crossentropy', optimizer='nadam', metrics=['acc', 'mse', 'mae'])
# fit the keras model on the dataset
#history = model.fit(inputs, inputs, validation_split=0.05, epochs=20, batch_size=32, verbose=1)
# Save everything
name = "cae_true-table"
# Saving history
with open(os.path.join('trained_models', "g3_" + name + "_{}".format(num_events) + "-history.pickle"), 'wb') as file_pi:
pickle.dump(history.history, file_pi)
# Saving the model
model.save(os.path.join('trained_models', "g3_" + name + "_{}".format(num_events) + ".hd5"))
print(history.history)
try:
plt.plot(history.history['loss'])
plt.savefig(os.path.join('plots', "g3_" + name + "_{}".format(num_events), name +"_loss.png"))
plt.clf()
plt.plot(history.history['acc'])
plt.savefig(os.path.join('plots', "g3_" + name + "_{}".format(num_events), name +"_acc.png"))
plt.clf()
plt.plot(history.history['mse'])
plt.savefig(os.path.join('plots', "g3_" + name + "_{}".format(num_events), name +"_mse.png"))
plt.clf()
plt.plot(history.history['mae'])
plt.savefig(os.path.join('plots', "g3_" + name + "_{}".format(num_events), name +"_mae.png"))
except Exception as ex:
print("(!) Error building plots ", ex)
'''