def load_model(ckpt_weights_file, model, learning_rate):
adam = Adagrad(lr=learning_rate, epsilon=1e-08, decay=0.0, clipnorm=1.)
model.compile(metrics=[], optimizer=adam)
model.load_model_weights(ckpt_weights_file)
kb.release_key('D')
kb.release_key('S')
def nothing():
kb.release_key('W')
kb.release_key('A')
kb.release_key('D')
kb.release_key('S')
key_press = {'W': forward, 'S': backward, 'A': left, 'D': right, '.': nothing}
if __name__ == '__main__':
model = load_model_weights('v1_nfs_ai')
print("Starting !!!")
time.sleep(1)
print("1")
time.sleep(1)
print("2")
time.sleep(1)
print("3")
time.sleep(1)
print("4")
print("GO go go go !!!!")
while (True):
current_view = grab_screen([0, 0, 800, 600])
reduced_view = cv2.resize(current_view, (shape[1], shape[0]),
@author: RAJAT
"""
import numpy as np
import cv2
from time import time
from keras.models import Sequential, Model
from keras.layers import *
from keras.callbacks import TensorBoard
from model import load_model_weights
from datacollect import load_train_data, shape, margin
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
model = load_model_weights("v4_nfs_iteration2_ai")
layer_dict = dict([(layer.name, layer) for layer in model.layers])
X_train, Y_train = load_train_data('third_batch')
model.summary()
test = Sequential()
test.add(layer_dict['right_input'])
test.add(layer_dict['conv2d_1'])
test.add(layer_dict['conv2d_2'])
#test.add(layer_dict['conv2d_3'])
#test.add(layer_dict['conv2d_4'])
#test.add(layer_dict['conv2d_5'])
side = 1
res = test.predict(X_train[side, :, :, :, :])
elif move == 'D':
kb.release_key('W')
kb.release_key('S')
kb.release_key('A')
kb.press_key('D')
else:
kb.release_key('W')
kb.release_key('S')
kb.release_key('A')
kb.release_key('D')
if __name__ == '__main__':
model = load_model_weights('v4_nfs_iteration2_ai')
print("Starting !!!")
time.sleep(1)
print("1")
time.sleep(1)
print("2")
time.sleep(1)
print("3")
time.sleep(1)
print("4")
print("GO go go go !!!!")
while (True):
current_view = grab_screen([0, 0, 800, 600])
reduced_view = cv2.resize(current_view, (shape[1], shape[0]),
Created on Mon Feb 5 20:23:46 2018
@author: RAJAT
"""
import numpy as np
from time import time
from keras.models import Sequential, Model
from keras.layers import *
from keras.callbacks import TensorBoard
from model import load_model_weights
from datacollect import load_train_data, shape, margin
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
model = load_model_weights("v3_nfs_ai")
layer_dict = dict([(layer.name, layer) for layer in model.layers])
X_train, Y_train = load_train_data('second_batch')
model.summary()
test = Sequential()
test.add(layer_dict['right_input'])
test.add(layer_dict['conv2d_1'])
test.add(layer_dict['conv2d_2'])
#test.add(layer_dict['conv2d_3'])
#test.add(layer_dict['conv2d_4'])
#test.add(layer_dict['conv2d_5'])
img_no = 8000
side = 0
plt.imshow(X_train[side, img_no, :, :, 0], cmap='gray')