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train.py
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train.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Jun 11 17:35:43 2019
@author: mancmanomyst
"""
import os
os.environ['TF_CPP_MIN_LOG_LEVEL']='3'
import numpy as np
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras.utils import np_utils
import matplotlib.pyplot as plt
with np.load("voxelgrid.npz") as data:
X_train=data["X_train"]
y_train=data["y_train"]
X_test=data["X_test"]
y_test=data["y_test"]
n_classes=40
Y_train=np_utils.to_categorical(y_train,n_classes)
Y_test=np_utils.to_categorical(y_test,n_classes)
X_train = X_train.reshape(-1, 16, 16, 16)
X_test = X_test.reshape(-1, 16, 16, 16)
model = Sequential()
model.add(Conv2D(32, (3, 3), padding='same',input_shape=X_train.shape[1:]))
model.add(Activation('relu'))
model.add(Conv2D(32, (3, 3),padding='same'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(64, (3, 3), padding='same'))
model.add(Activation('relu'))
model.add(Conv2D(64, (3, 3),padding='same'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(512))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(n_classes))
model.add(Activation('softmax'))
model.compile(loss='categorical_crossentropy',metrics=['accuracy'],optimizer='adam')
history = model.fit(X_train, Y_train, batch_size=128, epochs=20, verbose=2, validation_data=(X_test, Y_test))
plt.plot(history.history['acc'])
plt.plot(history.history['val_acc'])
plt.title('Model accuracy')
plt.ylabel('Accuracy')
plt.xlabel('Epoch')
plt.legend(['Train', 'Test'], loc='upper left')
plt.show()
# Plot training & validation loss values
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('Model loss')
plt.ylabel('Loss')
plt.xlabel('Epoch')
plt.legend(['Train', 'Test'], loc='upper left')
plt.show()
save_dir = "/home/mancmanomyst/modelneth5py/modelnet40_ply_hdf5_2048"
model_name = '3d_vox2.h5'
model_path = os.path.join(save_dir, model_name)
model.save(model_path)
print('Saved trained model at %s ' % model_path)