127 lines
5.9 KiB
Python
127 lines
5.9 KiB
Python
#!/usr/bin/env python3
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import container_support as cs
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import os
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import json
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import re
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import zipfile
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from keras.preprocessing.image import load_img, img_to_array
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import numpy as np
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from keras.layers import Input, Dense, merge
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from keras.models import Model
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from keras.layers import Convolution2D, MaxPooling2D, Reshape, BatchNormalization
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from keras.layers import Activation, Dropout, Flatten, Dense
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from keras import callbacks
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from tensorflow.python.client import device_lib
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def train():
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env = cs.TrainingEnvironment()
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print(device_lib.list_local_devices())
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os.system('mkdir -p logs')
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# ### Loading the files ###
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# ** You need to copy all your files to the directory where you are runing this notebook into a folder named "data" **
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numbers = re.compile(r'(\d+)')
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data = []
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def get_data(root,f):
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d = json.load(open(os.path.join(root,f)))
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if ('pilot/throttle' in d):
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return [d['user/mode'],d['user/throttle'],d['user/angle'],root,d['cam/image_array'],d['pilot/throttle'],d['pilot/angle']]
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else:
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return [d['user/mode'],d['user/throttle'],d['user/angle'],root,d['cam/image_array']]
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def numericalSort(value):
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parts = numbers.split(value)
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parts[1::2] = map(int, parts[1::2])
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return parts
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def unzip_file(root,f):
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zip_ref = zipfile.ZipFile(os.path.join(root,f), 'r')
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zip_ref.extractall(root)
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zip_ref.close()
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for root, dirs, files in os.walk('/opt/ml/input/data/train'):
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for f in files:
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if f.endswith('.zip'):
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unzip_file(root, f)
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for root, dirs, files in os.walk('/opt/ml/input/data/train'):
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data.extend([get_data(root,f) for f in sorted(files, key=numericalSort) if f.startswith('record') and f.endswith('.json')])
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# Normalize / correct data
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data = [d for d in data if d[1] > 0.1]
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for d in data:
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if d[1] < 0.2:
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d[1] = 0.2
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# ### Loading throttle and angle ###
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angle = [d[2] for d in data]
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throttle = [d[1] for d in data]
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angle_array = np.array(angle)
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throttle_array = np.array(throttle)
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if (len(data[0]) > 5):
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pilot_angle = [d[6] for d in data]
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pilot_throttle = [d[5] for d in data]
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pilot_angle_array = np.array(pilot_angle)
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pilot_throttle_array = np.array(pilot_throttle)
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else:
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pilot_angle = []
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pilot_throttle = []
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# ### Loading images ###
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images = np.array([img_to_array(load_img(os.path.join(d[3],d[4]))) for d in data],'f')
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# slide images vs orders
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if env.hyperparameters.get('with_slide', False):
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images = images[:len(images)-2]
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angle_array = angle_array[2:]
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throttle_array = throttle_array[2:]
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# ### Start training ###
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def linear_bin(a):
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a = a + 1
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b = round(a / (2/14))
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arr = np.zeros(15)
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arr[int(b)] = 1
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return arr
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logs = callbacks.TensorBoard(log_dir='logs', histogram_freq=0, write_graph=True, write_images=True)
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save_best = callbacks.ModelCheckpoint('/opt/ml/model/model_cat', monitor='angle_out_loss', verbose=1, save_best_only=True, mode='min')
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early_stop = callbacks.EarlyStopping(monitor='angle_out_loss',
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min_delta=.0005,
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patience=10,
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verbose=1,
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mode='auto')
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img_in = Input(shape=(120, 160, 3), name='img_in') # First layer, input layer, Shape comes from camera.py resolution, RGB
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x = img_in
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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
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x = Convolution2D(32, (5,5), strides=(2,2), activation='relu')(x) # 32 features, 5px5p kernel window, 2wx2h stride, relu activatiion
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x = Convolution2D(64, (5,5), strides=(2,2), activation='relu')(x) # 64 features, 5px5p kernal window, 2wx2h stride, relu
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x = Convolution2D(64, (3,3), strides=(2,2), activation='relu')(x) # 64 features, 3px3p kernal window, 2wx2h stride, relu
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x = Convolution2D(64, (3,3), strides=(1,1), activation='relu')(x) # 64 features, 3px3p kernal window, 1wx1h stride, relu
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# Possibly add MaxPooling (will make it less sensitive to position in image). Camera angle fixed, so may not to be needed
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x = Flatten(name='flattened')(x) # Flatten to 1D (Fully connected)
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x = Dense(100, activation='relu')(x) # Classify the data into 100 features, make all negatives 0
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x = Dropout(.1)(x)
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x = Dense(50, activation='relu')(x)
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x = Dropout(.1)(x) # Randomly drop out 10% of the neurons (Prevent overfitting)
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#categorical output of the angle
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callbacks_list = [save_best, early_stop, logs]
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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
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#continous output of throttle
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throttle_out = Dense(1, activation='relu', name='throttle_out')(x) # Reduce to 1 number, Positive number only
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angle_cat_array = np.array([linear_bin(a) for a in angle_array])
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model = Model(inputs=[img_in], outputs=[angle_out, throttle_out])
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model.compile(optimizer='adam',
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loss={'angle_out': 'categorical_crossentropy',
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'throttle_out': 'mean_absolute_error'},
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loss_weights={'angle_out': 0.9, 'throttle_out': .001})
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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)
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