steerSampleSTD=steer.std()
np.savez("steerstats.npz", [steerSampleMean, steerSampleSTD])
from testmodel import model
model.load_weights('Nweights.h5')
img_files=glob.glob(os.path.join(data_dir, 'imgs*.npz'))
imgs=np.zeros((100*len(img_files), 78, 128, 3)).astype('float32')
i=0
for imgfile in sorted(img_files):
imdata=np.load(imgfile)['arr_0'].astype('float32')
j=0
for tim in imdata:
im=tim[0:78, :]
immean=im.mean()
imdev=im.std()
imgs[i*100+j]=(im-immean)/imdev
j+=1
i+=1
for n in range(num_epochs):
print("starting epoch {0}".format(n))
h=model.fit([imgs], [(steer-steerSampleMean)/steerSampleSTD],
batch_size=25, epochs=1, verbose=1, validation_split=0.1, shuffle=True)
if n%save_epochs ==0 :
print("Saving epoch {0} to {1}".format(n, weightfile))
model.save_weights(weightfile, overwrite=True)
model.save_weights(weightfile, overwrite=True)
steerSampleMean = steer.mean()
steerSampleSTD = steer.std()
print(steerSampleMean)
print(steerSampleSTD)
from testmodel import model
img_files = glob.glob(os.path.join(data_dir, 'imgs*.npz'))
command_files = glob.glob(os.path.join(data_dir, 'commands*.npz'))
imgs = np.array((100, 96, 128, 3))
commands = np.array((100, 2))
for n in range(num_epochs):
print("starting epoch {0}".format(n))
for i, c in zip(sorted(img_files), sorted(command_files)):
print(i, c)
imgs = np.load(i)['arr_0']
commands = np.load(c)['arr_0']
h = model.fit([imgs],
[(commands[:, 0] - steerSampleMean) / steerSampleSTD],
batch_size=32,
epochs=1,
verbose=1,
validation_split=0.1,
shuffle=True)
if n % save_epochs == 0:
print("Saving epoch {0} to {1}".format(n, weightfile))
model.save_weights(weightfile, overwrite=True)
model.save_weights(weightfile, overwrite=True)