robocars/robocar-training
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Reformat code

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This commit is contained in:
Cyrille Nofficial 2020-02-17 19:11:29 +01:00
parent 84a8b11942
commit a5354e5653
1 changed files with 38 additions and 27 deletions
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65
src/tf_container/train_entry_point.py
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@ -19,10 +19,11 @@ from tensorflow.python.client import device_lib
def get_data(root_dir, filename):
print('load data from file '+ filename)
print('load data from file ' + filename)
d = json.load(open(os.path.join(root_dir, filename)))
if 'pilot/throttle' in d:
return [d['user/mode'], d['user/throttle'], d['user/angle'], root_dir, d['cam/image_array'], d['pilot/throttle'], d['pilot/angle']]
return [d['user/mode'], d['user/throttle'], d['user/angle'], root_dir, d['cam/image_array'],
d['pilot/throttle'], d['pilot/angle']]
else:
return [d['user/mode'], d['user/throttle'], d['user/angle'], root_dir, d['cam/image_array']]
@ -30,8 +31,8 @@ def get_data(root_dir, filename):
numbers = re.compile(r'(\d+)')
def unzip_file(root,f):
zip_ref = zipfile.ZipFile(os.path.join(root,f), 'r')
def unzip_file(root, f):
zip_ref = zipfile.ZipFile(os.path.join(root, f), 'r')
zip_ref.extractall(root)
zip_ref.close()
@ -54,11 +55,12 @@ def train():
unzip_file(root, f)
for root, dirs, files in os.walk('/opt/ml/input/data/train'):
data.extend([get_data(root,f) for f in sorted(files, key=str.lower) if f.startswith('record') and f.endswith('.json')])
data.extend(
[get_data(root, f) for f in sorted(files, key=str.lower) if f.startswith('record') and f.endswith('.json')])
# Normalize / correct data
#data = [d for d in data if d[1] > 0.1]
#for d in data:
# data = [d for d in data if d[1] > 0.1]
# for d in data:
# if d[1] < 0.2:
# d[1] = 0.2
@ -79,24 +81,25 @@ def train():
pilot_throttle = []
# ### Loading images ###
images = np.array([img_to_array(load_img(os.path.join(d[3],d[4]))) for d in data],'f')
images = np.array([img_to_array(load_img(os.path.join(d[3], d[4]))) for d in data], 'f')
# slide images vs orders
if env.hyperparameters.get('with_slide', False):
images = images[:len(images)-2]
images = images[:len(images) - 2]
angle_array = angle_array[2:]
throttle_array = throttle_array[2:]
# ### Start training ###
def linear_bin(a):
a = a + 1
b = round(a / (2/14))
b = round(a / (2 / 14))
arr = np.zeros(15)
arr[int(b)] = 1
return arr
logs = callbacks.TensorBoard(log_dir='logs', histogram_freq=0, write_graph=True, write_images=True)
save_best = callbacks.ModelCheckpoint('/opt/ml/model/model_cat', monitor='angle_out_loss', verbose=1, save_best_only=True, mode='min')
save_best = callbacks.ModelCheckpoint('/opt/ml/model/model_cat', monitor='angle_out_loss', verbose=1,
save_best_only=True, mode='min')
early_stop = callbacks.EarlyStopping(monitor='angle_out_loss',
min_delta=.0005,
patience=10,
@ -109,34 +112,42 @@ def train():
img_in = Input(shape=(128, 160, 3),
name='img_in') # First layer, input layer, Shape comes from camera.py resolution, RGB
x = img_in
x = Convolution2D(24, (5,5), strides=(2,2), activation='relu')(x) # 24 features, 5 pixel x 5 pixel kernel (convolution, feauture) window, 2wx2h stride, relu activation
x = Convolution2D(32, (5,5), strides=(2,2), activation='relu')(x) # 32 features, 5px5p kernel window, 2wx2h stride, relu activatiion
x = Convolution2D(64, (5,5), strides=(2,2), activation='relu')(x) # 64 features, 5px5p kernal window, 2wx2h stride, relu
x = Convolution2D(64, (3,3), strides=(2,2), activation='relu')(x) # 64 features, 3px3p kernal window, 2wx2h stride, relu
x = Convolution2D(64, (3,3), strides=(1,1), activation='relu')(x) # 64 features, 3px3p kernal window, 1wx1h stride, relu
x = Convolution2D(24, (5, 5), strides=(2, 2), activation='relu')(
x) # 24 features, 5 pixel x 5 pixel kernel (convolution, feauture) window, 2wx2h stride, relu activation
x = Convolution2D(32, (5, 5), strides=(2, 2), activation='relu')(
x) # 32 features, 5px5p kernel window, 2wx2h stride, relu activatiion
x = Convolution2D(64, (5, 5), strides=(2, 2), activation='relu')(
x) # 64 features, 5px5p kernal window, 2wx2h stride, relu
x = Convolution2D(64, (3, 3), strides=(2, 2), activation='relu')(
x) # 64 features, 3px3p kernal window, 2wx2h stride, relu
x = Convolution2D(64, (3, 3), strides=(1, 1), activation='relu')(
x) # 64 features, 3px3p kernal window, 1wx1h stride, relu
# Possibly add MaxPooling (will make it less sensitive to position in image). Camera angle fixed, so may not to be needed
x = Flatten(name='flattened')(x) # Flatten to 1D (Fully connected)
x = Dense(100, activation='relu')(x) # Classify the data into 100 features, make all negatives 0
x = Flatten(name='flattened')(x) # Flatten to 1D (Fully connected)
x = Dense(100, activation='relu')(x) # Classify the data into 100 features, make all negatives 0
x = Dropout(.1)(x)
x = Dense(50, activation='relu')(x)
x = Dropout(.1)(x) # Randomly drop out 10% of the neurons (Prevent overfitting)
#categorical output of the angle
x = Dropout(.1)(x) # Randomly drop out 10% of the neurons (Prevent overfitting)
# categorical output of the angle
callbacks_list = [save_best, early_stop, logs]
angle_out = Dense(15, activation='softmax', name='angle_out')(x) # Connect every input with every output and output 15 hidden units. Use Softmax to give percentage. 15 categories and find best one based off percentage 0.0-1.0
angle_out = Dense(15, activation='softmax', name='angle_out')(
x) # Connect every input with every output and output 15 hidden units. Use Softmax to give percentage. 15 categories and find best one based off percentage 0.0-1.0
#continous output of throttle
throttle_out = Dense(1, activation='relu', name='throttle_out')(x) # Reduce to 1 number, Positive number only
# continous output of throttle
throttle_out = Dense(1, activation='relu', name='throttle_out')(x) # Reduce to 1 number, Positive number only
angle_cat_array = np.array([linear_bin(a) for a in angle_array])
model = Model(inputs=[img_in], outputs=[angle_out, throttle_out])
model.compile(optimizer='adam',
loss={'angle_out': 'categorical_crossentropy',
loss={'angle_out': 'categorical_crossentropy',
'throttle_out': 'mean_absolute_error'},
loss_weights={'angle_out': 0.9, 'throttle_out': .001})
model.fit({'img_in':images},{'angle_out': angle_cat_array, 'throttle_out': throttle_array}, batch_size=32, epochs=100, verbose=1, validation_split=0.2, shuffle=True, callbacks=callbacks_list)
loss_weights={'angle_out': 0.9, 'throttle_out': .001})
model.fit({'img_in': images}, {'angle_out': angle_cat_array, 'throttle_out': throttle_array}, batch_size=32,
epochs=100, verbose=1, validation_split=0.2, shuffle=True, callbacks=callbacks_list)
# Save model for tensorflow using
builder = tf.saved_model.builder.SavedModelBuilder("myModel")
builder = tf.saved_model.builder.SavedModelBuilder("/opt/ml/model/tfModel")
# Tag the model, required for Go
builder.add_meta_graph_and_variables(sess, ["myTag"])