keras_g3_cae_true-table_merged-cl.py

# Keras Geant3 _ Convolutional Autoencoder _ True Table _ Merged Clusters
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

from data_tools import merge_clusters
from event_display import print_tabled_event


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...")

events_to_read = 100000

add_real_xy = False
inputs, answers, values = build_true_answers_train_set(data_file, events_to_read * 2, norm_func=norm_func, rnd_shift=((-4,4), (-4,4)) )

print(f"Inputs shape original = {np.shape(inputs)}")
print(f"max hit value = {np.max(inputs)}")


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)

# Merge clusters
print("Merging clusters...")
inputs, answers = merge_clusters(inputs, answers)

print(f"Inputs shape merged = {np.shape(inputs)}")
print(f"Total events prepare time = {time.time() - parse_start}")

print_tabled_event(inputs[0]*11)
print_tabled_event(answers[0]*11)
print("-----------------------------------")
print_tabled_event(inputs[1]*11)
print_tabled_event(answers[1]*11)


model = Sequential()
model.add(Input(shape=(12, 12, 1)))
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(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(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 = os.path.join("trained_models", "keras_g3_cae_true-table_merged-cl")

# Saving history
with open(name + "-history.pickle", 'wb') as file_pi:
    pickle.dump(history.history, file_pi)

# Saving the model
model.save(name + ".hd5")

print(history.history)

try:
    plt.plot(history.history['loss'])
    plt.show()
    plt.plot(history.history['acc'])
    plt.show()
    plt.plot(history.history['mse'])
    plt.show()
    plt.plot(history.history['mae'])
    plt.show()
    # plt.plot(history.history['cosine'])
    #plt.show()
except Exception as ex:
    print("(!) Error building plots ", ex)