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main_deep_nn_6.py
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main_deep_nn_6.py
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import keras
from keras.models import Sequential
# from keras.metrics import
from keras.layers import Dense, Conv1D, Flatten, MaxPool2D, Dropout, MaxPooling1D
from sklearn.preprocessing import MinMaxScaler
# create model
import cv2
import os
import numpy as np
from os import listdir
from os.path import isfile, join
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
# train_files = [f for f in listdir('data_train_preprocessed_reshape') if isfile(join('data_train', f))]
from nn_final_image.ETools import EImage
train_files = [f for f in listdir('final_data/pre_preprocessed_train') if isfile(join('final_data/pre_preprocessed_train', f))]
# test_files = [f for f in listdir('test_2') if isfile(join('test_2', f))]
X_TRAIN_DATA = []
Y_TRAIN_label = []
# X_TEST_DATA = []
# Y_TEST_label = []
for reshape_file in train_files:
if '.jpg' not in reshape_file:
continue
# x_image = cv2.imread('preprocessed_train/' + reshape_file)
x_image = EImage.read_image('final_data/pre_preprocessed_train/' + reshape_file, if_read_as_grayscale=True)
# print(x_image.shape)
y_label = int(reshape_file.split('_')[0])
X_TRAIN_DATA.append(x_image)
y_data = None
# 5, 10, 25, 50 ,100
if y_label == 0:
y_data = 0
else:
y_data = 1
# else:
# print('ERROR-Y_TRAIN_label -> unvalid classlabel ', reshape_file)
# print('Y_TRAIN_label: ', y_label)
# print('Y_TRAIN_label.type: ', type(y_label))
# break
Y_TRAIN_label.append(y_data)
# for reshape_file in test_files:
# x_image = cv2.imread('test_2/' + reshape_file)
# y_label = int(reshape_file.split('_')[0])
# X_TEST_DATA.append(x_image)
# y_data = None
# 5, 10, 25, 50 ,100
# if y_label == 5:
# y_data = [1, 0, 0, 0, 0]
# elif y_label == 10:
# y_data = [0, 1, 0, 0, 0]
# elif y_label == 25:
# y_data = [0, 0, 1, 0, 0]
# elif y_label == 50:
# y_data = [0, 0, 0, 1, 0]
# elif y_label == 100:
# y_data = [0, 0, 0, 0, 1]
# else:
# print('ERROR-Y_TEST_label -> unvalid classlabel ', reshape_file)
# print('Y_TEST_label: ', y_label)
# print('Y_TEST_label.type: ', type(y_label))
# break
#
# Y_TEST_label.append(y_data)
# scaler_x = MinMaxScaler()
# scaler_y = MinMaxScaler()
X_TRAIN_DATA = np.array(X_TRAIN_DATA)
print('X_TRAIN_DATA.shape {}'.format(X_TRAIN_DATA.shape))
# X_TRAIN_DATA = X_TRAIN_DATA.reshape(X_TRAIN_DATA.shape[0], 512, 512, 1)
# print('X_TRAIN_DATA.shape {}'.format(X_TRAIN_DATA.shape))
# Y_TRAIN_label = np.array(Y_TRAIN_label)
# X_TEST_DATA = np.array(X_TEST_DATA)
# Y_TEST_label = np.array(Y_TEST_label)
# nsamples, nx, ny = X_DATA.shape
# X_DATA = X_DATA.reshape((nsamples,nx*ny))
X_TRAIN_DATA_SCALED = X_TRAIN_DATA / 255.
# X_TEST_DATA_SCALED = X_TEST_DATA / 255.
# scaler_x.fit_transform(X_DATA)
# scaler_y.fit_transform(Y_label)
# X_DATA_SCALED = scaler_x.transform(X_DATA)
# Y_label_SCALED = scaler_y.transform(Y_label)
# scalers = {}
# for i in range(X_DATA.shape[2]):
# scalers[i] = StandardScaler()
# X_DATA[:, i, :] = scalers[i].fit_transform(X_DATA[:, i, :])
print('X_DATA_SCALED.shape = {}'.format(X_TRAIN_DATA_SCALED.shape))
#
# scaler = StandardScaler()
# num_instances, num_time_steps, num_features = X_DATA.shape
# X_DATA = np.reshape(X_DATA, shape=(-1, num_features))
# X_DATA = scaler.fit_transform(X_DATA)
print('X_TRAIN_DATA_SCALED.shape = {}'.format(X_TRAIN_DATA_SCALED.shape))
# print('X_TEST_DATA_SCALED.shape = {}'.format(X_TEST_DATA_SCALED.shape))
print('Y_label.shape = {}'.format(len(Y_TRAIN_label)))
# X_train, X_test, y_train, y_test = train_test_split(X_DATA_SCALED, Y_label, test_size=0.33)
model = Sequential()
# add model layers
# model.add(Conv2D(32, kernel_size=3, activation='relu', input_shape=(80, 80,3)))
# model.add(Conv2D(32, kernel_size=3, activation='relu'))
# model.add(MaxPooling2D(pool_size=(2,2),strides=(2, 2)))
# model.add(Flatten())
# model.add(Dropout(0.2))
# model.add(Dense(3, activation='softmax'))
model.add(Conv1D(32, kernel_size=5, activation='relu', input_shape=(256, 256)))
# model.add(Conv2D(32, kernel_size=5, activation='relu', input_shape=(80, 80, 3)))
# model.add(Conv2D(64, kernel_size=3, activation='relu', input_shape=(80, 80, 3)))
model.add(MaxPooling1D(pool_size=2,strides=2))
model.add(Conv1D(64, kernel_size=5, activation='relu'))
model.add(MaxPooling1D(pool_size=2))
model.add(Flatten())
# model.add(Dropout(0.2))
model.add(Dense(1000, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
model.compile(optimizer='adam', loss=keras.losses.binary_crossentropy, metrics=['accuracy'])
print(X_TRAIN_DATA_SCALED)
# model.fit(X_TRAIN_DATA_SCALED, Y_TRAIN_label, batch_size=10, epochs=10,shuffle=True)
model.fit(X_TRAIN_DATA_SCALED, Y_TRAIN_label, batch_size=10, epochs=10,validation_split=0.2,shuffle=True)
# model.
# res = model.predict(X_TEST_DATA_SCALED)
# print(res)
# print(type(res))
# print(res.shape)
# scores = model.evaluate(X_TEST_DATA, Y_TEST_label, verbose=0)
# print("score: ", scores)
# print("%s: %.2f%%" % (model.metrics_names[1], scores[1] * 100))
model.save('model_deep_nn_6_pre.h5')