In [1]:
import os, cv2, random, json
import numpy as np
import pandas as pd
np.random.seed(2016)
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
import matplotlib.pyplot as plt
from matplotlib import ticker
import seaborn as sns
%matplotlib inline
from keras.models import Sequential, Model
from keras.layers import Input, Dropout, Flatten, Convolution2D, MaxPooling2D, ZeroPadding2D, Dense, Activation
from keras.layers import merge, Convolution1D, BatchNormalization, Reshape, Permute
from keras.optimizers import RMSprop, Adam, Adamax, Nadam, SGD, Adadelta
from keras.callbacks import ModelCheckpoint, Callback, EarlyStopping
from keras.utils import np_utils
from keras.regularizers import l2
from keras import backend as K
from keras.preprocessing.image import ImageDataGenerator
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ROWS = 120
COLS = 320
CHANNELS = 3
DIR = 'data/IMG/'
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data = pd.read_csv('data/driving_log.csv', header=None,
names=['center', 'left', 'right', 'angle', 'throttle', 'break', 'speed'])
print(data.ix[0].center)
data.sample()
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def img_id(path):
return path.split('/IMG/')[1]
image_paths = data.center.apply(img_id).values.tolist()
image_paths[:5]
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# y_all = data[['angle', 'throttle']].values
y_all = data.angle.values
n_samples = y_all.shape[0]
print("Training Model with {} Samples".format(n_samples))
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def read_image(path):
img = cv2.imread(path, cv2.IMREAD_COLOR)
img = img[40:160, 0:320] ## Cropping top section of image, just useless noise
# img = cv2.imread(path, cv2.IMREAD_GRAYSCALE)
# img = np.expand_dims(img, axis=2)
return img[:,:,::-1]
X_all = np.ndarray((n_samples, ROWS, COLS, CHANNELS), dtype=np.uint8)
for i, path in enumerate(image_paths):
DIR+path
img = read_image(DIR+path)
X_all[i] = img
print(X_all.shape)
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for img in X_all[:3]:
plt.imshow(img)
plt.show()
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X_train, X_test, y_train, y_test = train_test_split(
X_all, y_all, test_size=0.20, random_state=23)
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def fit_gen(data, batch_size):
while 1:
x = np.ndarray((batch_size, ROWS, COLS, CHANNELS), dtype=np.uint8)
y = np.zeros(batch_size)
i=0
for line in data.iterrows():
path = line[1].center.split('/IMG/')[1]
x[i] = read_image(DIR+path)
y[i] = line[1].angle
i+=1
if i == batch_size:
i=0
yield (x, y)
x = np.ndarray((batch_size, ROWS, COLS, CHANNELS), dtype=np.uint8)
y = np.zeros(batch_size)
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def rmse(y_true, y_pred):
return K.sqrt(K.mean(K.square(y_pred - y_true), axis=-1))
def get_model():
lr = 0.0001
weight_init='glorot_normal'
opt = RMSprop(lr)
loss = 'mean_squared_error'
model = Sequential()
model.add(BatchNormalization(mode=2, axis=1, input_shape=(ROWS, COLS, CHANNELS)))
model.add(Convolution2D(3, 3, 3, init=weight_init, border_mode='valid', activation='relu', input_shape=(ROWS, COLS, CHANNELS)))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Convolution2D(9, 3, 3, init=weight_init, border_mode='valid', activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Convolution2D(18, 3, 3, init=weight_init, border_mode='valid', activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Convolution2D(32, 3, 3, init=weight_init, border_mode='valid', activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(80, activation='relu', init=weight_init))
# model.add(Dropout(0.5))
model.add(Dense(15, activation='relu', init=weight_init))
model.add(Dropout(0.25))
model.add(Dense(1, init=weight_init, activation='linear'))
model.compile(optimizer=opt, loss=loss)
return model
model = get_model()
model.summary()
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nb_epoch = 30
batch_size = 64
### Creating Validation Data
X_train, X_test, y_train, y_test = train_test_split(
X_all, y_all, test_size=0.20, random_state=23)
# Callbacks
early_stopping = EarlyStopping(monitor='val_loss', patience=8, verbose=1, mode='auto')
save_weights = ModelCheckpoint('new_model.h5', monitor='val_loss', save_best_only=True)
model.fit_generator(fit_gen(data, 32),
samples_per_epoch=data.shape[0], nb_epoch=nb_epoch,
validation_data=(X_test, y_test), callbacks=[save_weights, early_stopping])
model.fit(X_all, y_all, batch_size=batch_size, nb_epoch=nb_epoch,
validation_data=(X_test, y_test), verbose=1, shuffle=True, callbacks=[save_weights, early_stopping])
preds = model.predict(X_test, verbose=1)
print( "Test MSE: {}".format(mean_squared_error(y_test, preds)))
print( "Test RMSE: {}".format(np.sqrt(mean_squared_error(y_test, preds))))
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js = model.to_json()
with open('model.json', 'w') as outfile:
json.dump(js, outfile)
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