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Copy pathDLmodel_unsegmented_dataset.py
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DLmodel_unsegmented_dataset.py
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#!/bin/env python3
#SBATCH -N 1 # No. of computers you wanna use. Typically 1
#SBATCH -n 2 # No. of CPU cores you wanna use. Typically 1
#SBATCH -p gpu # This flag specifies that you wanna use GPU and not CPU
#SBATCH -o roadseg.out # output file name, in case your program has anything to output (like print, etc)
#SBATCH -t 24:00:00 # Amount of time
#SBATCH --gres=gpu:2 # No. of GPU cores you wanna use. Usually 2-3
import numpy
import pandas
import cv2
import math
import os
from keras.constraints import maxnorm
from keras.models import Sequential
from keras.layers import Dense
from keras.models import model_from_json
from keras.layers import Dropout
from keras.layers import Flatten
from keras.layers.convolutional import Conv2D
from keras.layers.convolutional import MaxPooling2D
from sklearn.cross_validation import cross_val_score
from sklearn.cross_validation import KFold
from sklearn.preprocessing import LabelEncoder
from sklearn.pipeline import Pipeline
from keras.wrappers.scikit_learn import KerasClassifier
from keras.utils import np_utils
from keras import backend as K
K.set_image_dim_ordering('th')
# fix random seed for reproducibility
seed = 7
numpy.random.seed(seed)
K = []
# load the dataset
dataframe = pandas.read_csv('road_dataset.csv', header=1)
dataset = dataframe.values
data = dataset[20000:40000,:]
# X_old = dataset[20000:40000,0]
for i in data[:,0]:
img = cv2.imread(i)
img = cv2.resize(img, (28,28))
K.append(img)
X = numpy.array(K)
look_back = 30
Y = numpy.array(data[:,2:4])
# split into train and test sets
train_size = int(len(data) * 0.67)
test_size = len(data) - train_size
X_train = X[0:(train_size)-look_back-1]
X_test = X[(train_size):len(X)-look_back-1]
train = data[0:train_size]
test = data[train_size:len(data)]
Y_train = Y[0:train_size,:]
Y_test = Y[train_size:len(dataset),:]
# convert an array of values into a dataset matrix
def create_dataset(dataset, look_back=1):
dataY = []
for i in range(len(dataset)-look_back-1):
dataY.append(dataset[i + look_back, :])
return numpy.array(dataY)
# reshape into X=t and Y=t+1
Y_train = create_dataset(Y_train, look_back)
Y_test = create_dataset(Y_test, look_back)
# reshape to be [samples][pixels][width][height]
X_train = X_train.reshape(X_train.shape[0], 3, 28, 28).astype('float32')
X_test = X_test.reshape(X_test.shape[0], 3, 28, 28).astype('float32')
# normalize inputs from 0-255 to 0-1
X_train = X_train / 255
X_test = X_test / 255
num_classes = 2
def model():
# create model
model = Sequential()
model.add(Conv2D(32,3, 3, border_mode='same', input_shape=(3, 28, 28), activation='relu'))
model.add(Dropout(0.15))
model.add(Conv2D(32,3, 3, activation='relu', border_mode='same'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(64,3, 3, activation='relu', border_mode='same'))
model.add(Dropout(0.15))
model.add(Conv2D(64,3, 3, activation='relu', border_mode='same'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(128,3, 3, activation='relu', border_mode='same'))
model.add(Dropout(0.15))
model.add(Conv2D(128,3, 3, activation='relu', border_mode='same'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dropout(0.15))
model.add(Dense(1024, activation='relu', W_constraint=maxnorm(3)))
model.add(Dropout(0.15))
model.add(Dense(512, activation='relu', W_constraint=maxnorm(3)))
model.add(Dropout(0.15))
model.add(Dense(num_classes))
model.compile(loss='mean_squared_error', optimizer='adam', metrics=['accuracy'])
return model
# build the model
model = model()
# Fit the model
model.fit(X_train, Y_train, validation_data=(X_test, Y_test), nb_epoch=10, batch_size=150, verbose=2)
# Final evaluation of the model
scores = model.evaluate(X_test, Y_test, verbose=0)
print("Error: %.2f%%" % (100-scores[1]*100))
# Estimate model performance
# trainScore = model.evaluate(X_train, Y_train, verbose=0)
# print('Train Score: %.2f MSE (%.2f RMSE)' % (trainScore, math.sqrt(trainScore)))
# testScore = model.evaluate(X_test, Y_test, verbose=0)
# print('Test Score: %.2f MSE (%.2f RMSE)' % (testScore, math.sqrt(testScore)))
# generate predictions for training
trainPredict = model.predict(X_train)
testPredict = model.predict(X_test)
# shift train predictions for plotting
trainPredictPlot = numpy.empty_like(dataset)
trainPredictPlot[:, :] = numpy.nan
trainPredictPlot[look_back:len(trainPredict)+look_back, :] = trainPredict
# shift test predictions for plotting
testPredictPlot = numpy.empty_like(dataset)
testPredictPlot[:, :] = numpy.nan
testPredictPlot[len(trainPredict)+(look_back*2)+1:len(dataset)-1, :] = testPredict
# plot baseline and predictions
plt.plot(dataset)
plt.plot(trainPredictPlot)
plt.plot(testPredictPlot)
plt.show()
# serialize model to JSON
model_json = model.to_json()
with open("model.json", "w") as json_file:
json_file.write(model_json)
# serialize weights to HDF5
model.save_weights("model.h5")
#print("Saved model to disk")