Python numpy.alen() Examples
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Example #1
Source File: AntBulletEnv.py From FitML with MIT License | 6 votes |
def add_noise_to_model(targetModel,largeNoise = False): #noisy_model = keras.models.clone_model(action_predictor_model) #noisy_model.set_weights(action_predictor_model.get_weights()) #print("Adding Noise to actor") #largeNoise = last_game_average < memoryR.mean() sz = len(noisy_model.layers) #if largeNoise: # print("Setting Large Noise!") for k in range(sz): w = targetModel.layers[k].get_weights() if np.alen(w) >0 : #print("k==>",k) if USE_GAUSSIAN_NOISE: w[0] = add_gaussian_noise(w[0],big_sigma,largeNoise) else: w[0] = add_noise_simple(w[0],largeNoise) targetModel.layers[k].set_weights(w) return targetModel
Example #2
Source File: ROBOTIC_Template_Experimental_v0.1.py From FitML with MIT License | 6 votes |
def reset_noisy_model(): sz = len(noisy_model.layers) #if largeNoise: # print("Setting Large Noise!") for k in range(sz): w = noisy_model.layers[k].get_weights() apW = action_predictor_model.layers[k].get_weights() if np.alen(w) >0: w[0] = reset_noisy_model_weights_to_apWeights(apW[0]) noisy_model.layers[k].set_weights(w) #print("w",w) #print("apW",apW) # --- Parameter Noising
Example #3
Source File: ROBOTIC_Template_Experimental_v0.1.py From FitML with MIT License | 6 votes |
def add_noise_to_model(targetModel,largeNoise = False): #noisy_model = keras.models.clone_model(action_predictor_model) #noisy_model.set_weights(action_predictor_model.get_weights()) #print("Adding Noise to actor") #largeNoise = last_game_average < memoryR.mean() sz = len(noisy_model.layers) #if largeNoise: # print("Setting Large Noise!") for k in range(sz): w = targetModel.layers[k].get_weights() if np.alen(w) >0 : #print("k==>",k) w[0] = add_noise(w[0],largeNoise) targetModel.layers[k].set_weights(w) return targetModel
Example #4
Source File: RoboschoolHalfCheetah_v1.py From FitML with MIT License | 6 votes |
def add_noise_to_model(targetModel,largeNoise = False): #noisy_model = keras.models.clone_model(action_predictor_model) #noisy_model.set_weights(action_predictor_model.get_weights()) #print("Adding Noise to actor") #largeNoise = last_game_average < memoryR.mean() sz = len(noisy_model.layers) #if largeNoise: # print("Setting Large Noise!") for k in range(sz): w = targetModel.layers[k].get_weights() if np.alen(w) >0 : #print("k==>",k) w[0] = add_noise(w[0],largeNoise) targetModel.layers[k].set_weights(w) return targetModel
Example #5
Source File: _MainAlgo_v4.2.py From FitML with MIT License | 6 votes |
def add_noise_to_model(targetModel,largeNoise = False): #noisy_model = keras.models.clone_model(action_predictor_model) #noisy_model.set_weights(action_predictor_model.get_weights()) #print("Adding Noise to actor") #largeNoise = last_game_average < memoryR.mean() sz = len(noisy_model.layers) #if largeNoise: # print("Setting Large Noise!") for k in range(sz): w = targetModel.layers[k].get_weights() if np.alen(w) >0 : #print("k==>",k) if USE_GAUSSIAN_NOISE: w[0] = add_gaussian_noise(w[0],big_sigma,largeNoise) else: w[0] = add_noise_simple(w[0],largeNoise) targetModel.layers[k].set_weights(w) return targetModel
Example #6
Source File: Walker2D.v2.0.py From FitML with MIT License | 6 votes |
def add_noise_to_model(largeNoise = False): #noisy_model = keras.models.clone_model(action_predictor_model) #noisy_model.set_weights(action_predictor_model.get_weights()) #print("Adding Noise to actor") #largeNoise = last_game_average < memoryR.mean() sz = len(noisy_model.layers) #if largeNoise: # print("Setting Large Noise!") for k in range(sz): w = noisy_model.layers[k].get_weights() #print("w ==>", w) if np.alen(w) >0: w[0] = add_noise_simple(w[0],largeNoise) noisy_model.layers[k].set_weights(w) return noisy_model # --- Parameter Noising
Example #7
Source File: MainAlgo_PR_v1.0.py From FitML with MIT License | 6 votes |
def add_noise_to_model(targetModel,largeNoise = False): #noisy_model = keras.models.clone_model(action_predictor_model) #noisy_model.set_weights(action_predictor_model.get_weights()) #print("Adding Noise to actor") #largeNoise = last_game_average < memoryR.mean() sz = len(noisy_model.layers) #if largeNoise: # print("Setting Large Noise!") for k in range(sz): w = targetModel.layers[k].get_weights() if np.alen(w) >0 : #print("k==>",k) if USE_GAUSSIAN_NOISE: w[0] = add_gaussian_noise(w[0],big_sigma,largeNoise) else: w[0] = add_noise_simple(w[0],largeNoise) targetModel.layers[k].set_weights(w) return targetModel
Example #8
Source File: Main_algo.py From FitML with MIT License | 6 votes |
def add_noise_to_model(targetModel,largeNoise = False): #noisy_model = keras.models.clone_model(action_predictor_model) #noisy_model.set_weights(action_predictor_model.get_weights()) #print("Adding Noise to actor") #largeNoise = last_game_average < memoryR.mean() sz = len(noisy_model.layers) #if largeNoise: # print("Setting Large Noise!") for k in range(sz): w = targetModel.layers[k].get_weights() if np.alen(w) >0 : #print("k==>",k) w[0] = add_noise(w[0],largeNoise) targetModel.layers[k].set_weights(w) return targetModel
Example #9
Source File: Main_algo.py From FitML with MIT License | 6 votes |
def reset_noisy_model(): sz = len(noisy_model.layers) #if largeNoise: # print("Setting Large Noise!") for k in range(sz): w = noisy_model.layers[k].get_weights() apW = action_predictor_model.layers[k].get_weights() if np.alen(w) >0: w[0] = reset_noisy_model_weights_to_apWeights(apW[0]) noisy_model.layers[k].set_weights(w) #print("w",w) #print("apW",apW) # --- Parameter Noising
Example #10
Source File: Hopper_v1.0.py From FitML with MIT License | 6 votes |
def add_noise_to_model(largeNoise = False): #noisy_model = keras.models.clone_model(action_predictor_model) #noisy_model.set_weights(action_predictor_model.get_weights()) #print("Adding Noise to actor") #largeNoise = last_game_average < memoryR.mean() sz = len(noisy_model.layers) #if largeNoise: # print("Setting Large Noise!") for k in range(sz): w = noisy_model.layers[k].get_weights() #print("w ==>", w) if np.alen(w) >0: w[0] = add_noise_simple(w[0],largeNoise) noisy_model.layers[k].set_weights(w) return noisy_model # --- Parameter Noising
Example #11
Source File: LunarLanderContinuous_v1.0.py From FitML with MIT License | 6 votes |
def add_noise_to_model(targetModel,largeNoise = False): #noisy_model = keras.models.clone_model(action_predictor_model) #noisy_model.set_weights(action_predictor_model.get_weights()) #print("Adding Noise to actor") #largeNoise = last_game_average < memoryR.mean() sz = len(noisy_model.layers) #if largeNoise: # print("Setting Large Noise!") for k in range(sz): w = targetModel.layers[k].get_weights() if np.alen(w) >0 : #print("k==>",k) w[0] = add_noise(w[0],largeNoise) targetModel.layers[k].set_weights(w) return targetModel
Example #12
Source File: LunarLanderContinuous_v1.0.py From FitML with MIT License | 6 votes |
def reset_noisy_model(): sz = len(noisy_model.layers) #if largeNoise: # print("Setting Large Noise!") for k in range(sz): w = noisy_model.layers[k].get_weights() apW = action_predictor_model.layers[k].get_weights() if np.alen(w) >0: w[0] = reset_noisy_model_weights_to_apWeights(apW[0]) noisy_model.layers[k].set_weights(w) #print("w",w) #print("apW",apW) # --- Parameter Noising
Example #13
Source File: BipedalWalker_v3.0.py From FitML with MIT License | 6 votes |
def add_noise_to_model(targetModel,largeNoise = False): #noisy_model = keras.models.clone_model(action_predictor_model) #noisy_model.set_weights(action_predictor_model.get_weights()) #print("Adding Noise to actor") #largeNoise = last_game_average < memoryR.mean() sz = len(noisy_model.layers) #if largeNoise: # print("Setting Large Noise!") for k in range(sz): w = targetModel.layers[k].get_weights() if np.alen(w) >0 : #print("k==>",k) if USE_GAUSSIAN_NOISE: w[0] = add_gaussian_noise(w[0],big_sigma,largeNoise) else: w[0] = add_noise_simple(w[0],largeNoise) targetModel.layers[k].set_weights(w) return targetModel
Example #14
Source File: Main_algo.py From FitML with MIT License | 5 votes |
def train_noisy_actor(): tX = (memoryS) tY = (memoryA) tW = (memoryW) train_A = np.random.randint(tY.shape[0],size=int(min(experience_replay_size,np.alen(tY) ))) tX = tX[train_A,:] tY = tY[train_A,:] tW = tW[train_A,:] noisy_model.fit(tX,tY, batch_size=mini_batch, nb_epoch=training_epochs,verbose=0)
Example #15
Source File: RoboschoolHalfCheetah_v1.py From FitML with MIT License | 5 votes |
def train_noisy_actor(): tX = (memoryS) tY = (memoryA) tW = (memoryW) train_A = np.random.randint(tY.shape[0],size=int(min(experience_replay_size,np.alen(tY) ))) tX = tX[train_A,:] tY = tY[train_A,:] tW = tW[train_A,:] noisy_model.fit(tX,tY, batch_size=mini_batch, nb_epoch=training_epochs,verbose=0)
Example #16
Source File: LunarLanderContinuous_v1.0.py From FitML with MIT License | 5 votes |
def scale_weights(memR,memW): rmax = memR.max() rmin = memR.min() reward_range = math.fabs(rmax - rmin ) if reward_range == 0: reward_range = 10 for i in range(np.alen(memR)): memW[i][0] = math.fabs(memR[i][0]-rmin)/reward_range memW[i][0] = max(memW[i][0],0.001) #print("memW %5.2f reward %5.2f rmax %5.2f rmin %5.2f "%(memW[i][0],memR[i][0],rmax,rmin)) #print("memW",memW) return memW
Example #17
Source File: LunarLanderContinuous_v1.0.py From FitML with MIT License | 5 votes |
def pr_actor_experience_replay(memSA,memR,memS,memA,memW,num_epochs=1): tSA = (memSA) tR = (memR) tX = (memS) tY = (memA) tW = (memW) tX_train = np.zeros(shape=(1,num_env_variables)) tY_train = np.zeros(shape=(1,num_env_actions)) for i in range(np.alen(tR)): pr = predictTotalRewards(tX[i],GetRememberedOptimalPolicy(tX[i])) #print ("tR[i]",tR[i],"pr",pr) d = math.fabs( memoryR.max() - pr) tW[i]= 0.0000000000000005 if (tR[i]>pr): tW[i]=0.15 if (tR[i]>pr+d/2): tW[i] = 1 if tW[i]> np.random.rand(1): tX_train = np.vstack((tX_train,tX[i])) tY_train = np.vstack((tY_train,tY[i])) tX_train = tX_train[1:] tY_train = tY_train[1:] print("%8d were better After removing first element"%np.alen(tX_train)) if np.alen(tX_train)>0: action_predictor_model.fit(tX_train,tY_train, batch_size=mini_batch, nb_epoch=num_epochs,verbose=0)
Example #18
Source File: Main_algo.py From FitML with MIT License | 5 votes |
def pr_actor_experience_replay(memSA,memR,memS,memA,memW,num_epochs=1): tSA = (memSA) tR = (memR) tX = (memS) tY = (memA) tW = (memW) tX_train = np.zeros(shape=(1,num_env_variables)) tY_train = np.zeros(shape=(1,num_env_actions)) for i in range(np.alen(tR)): pr = predictTotalRewards(tX[i],GetRememberedOptimalPolicy(tX[i])) #print ("tR[i]",tR[i],"pr",pr) d = math.fabs( memoryR.max() - pr) tW[i]= 0.0000000000000005 if (tR[i]>pr): tW[i]=0.15 if (tR[i]>pr+d/2): tW[i] = 1 if tW[i]> np.random.rand(1): tX_train = np.vstack((tX_train,tX[i])) tY_train = np.vstack((tY_train,tY[i])) tX_train = tX_train[1:] tY_train = tY_train[1:] print("%8d were better After removing first element"%np.alen(tX_train)) if np.alen(tX_train)>0: action_predictor_model.fit(tX_train,tY_train, batch_size=mini_batch, nb_epoch=num_epochs,verbose=0)
Example #19
Source File: LunarLanderContinuous_v1.0.py From FitML with MIT License | 5 votes |
def train_noisy_actor(): tX = (memoryS) tY = (memoryA) tW = (memoryW) train_A = np.random.randint(tY.shape[0],size=int(min(experience_replay_size,np.alen(tY) ))) tX = tX[train_A,:] tY = tY[train_A,:] tW = tW[train_A,:] noisy_model.fit(tX,tY, batch_size=mini_batch, nb_epoch=training_epochs,verbose=0)
Example #20
Source File: LunarLander_v1.py From FitML with MIT License | 5 votes |
def train_noisy_actor(): tX = (memoryS) tY = (memoryA) tW = (memoryW) train_A = np.random.randint(tY.shape[0],size=int(min(experience_replay_size,np.alen(tY) ))) tX = tX[train_A,:] tY = tY[train_A,:] tW = tW[train_A,:] noisy_model.fit(tX,tY, batch_size=mini_batch, nb_epoch=training_epochs,verbose=0)
Example #21
Source File: Main_algo.py From FitML with MIT License | 5 votes |
def scale_weights(memR,memW): rmax = memR.max() rmin = memR.min() reward_range = math.fabs(rmax - rmin ) if reward_range == 0: reward_range = 10 for i in range(np.alen(memR)): memW[i][0] = math.fabs(memR[i][0]-rmin)/reward_range memW[i][0] = max(memW[i][0],0.001) #print("memW %5.2f reward %5.2f rmax %5.2f rmin %5.2f "%(memW[i][0],memR[i][0],rmax,rmin)) #print("memW",memW) return memW
Example #22
Source File: RoboschoolHalfCheetah_v1.py From FitML with MIT License | 5 votes |
def reset_noisy_model(): sz = len(noisy_model.layers) #if largeNoise: # print("Setting Large Noise!") for k in range(sz): w = noisy_model.layers[k].get_weights() apW = action_predictor_model.layers[k].get_weights() if np.alen(w) >0: w[0] = reset_noisy_model_weights_to_apWeights(apW[0]) noisy_model.layers[k].set_weights(w) #print("w",w) #print("apW",apW)
Example #23
Source File: MainAlgo_PR_v1.0.py From FitML with MIT License | 5 votes |
def scale_weights(memR,memW): rmax = memR.max() rmin = memR.min() reward_range = math.fabs(rmax - rmin ) if reward_range == 0: reward_range = 10 for i in range(np.alen(memR)): memW[i][0] = math.fabs(memR[i][0]-rmin)/reward_range memW[i][0] = max(memW[i][0],0.001) #print("memW %5.2f reward %5.2f rmax %5.2f rmin %5.2f "%(memW[i][0],memR[i][0],rmax,rmin)) #print("memW",memW) return memW
Example #24
Source File: MainAlgo_PR_v1.0.py From FitML with MIT License | 5 votes |
def reset_noisy_model(): sz = len(noisy_model.layers) #if largeNoise: # print("Setting Large Noise!") for k in range(sz): w = noisy_model.layers[k].get_weights() apW = action_predictor_model.layers[k].get_weights() if np.alen(w) >0: w[0] = reset_noisy_model_weights_to_apWeights(apW[0]) noisy_model.layers[k].set_weights(w) #print("w",w) #print("apW",apW)
Example #25
Source File: Hopper_v1.0.py From FitML with MIT License | 5 votes |
def add_controlled_noise(largeNoise = False): tR = (memoryR) tX = (memoryS) tY = (memoryA) tW = (memoryW) train_C = np.random.randint(tY.shape[0],size=100) tX = tX[train_C,:] tY_old = tY[train_C,:] tY_new = tY[train_C,:] diffs = np.zeros(np.alen(tX)) delta = 1000 deltaCount = 0 while delta > 1: #noisy_model.set_weights(action_predictor_model.get_weights()) add_noise_to_model(True) for i in range(np.alen(tX)): a = GetRememberedOptimalPolicy(tX[i]) b = GetRememberedOptimalPolicyFromNoisyModel(tX[i]) a = a.flatten() b = b.flatten() c = np.abs(a-b) diffs[i] = c.mean() delta = np.average (diffs) deltaCount+=1 print("Tried x time ", deltaCount,"delta =", delta) #Play the game 500 times
Example #26
Source File: Hopper_v1.0.py From FitML with MIT License | 5 votes |
def train_noisy_actor(): tX = (memoryS) tY = (memoryA) tW = (memoryW) train_A = np.random.randint(tY.shape[0],size=int(min(experience_replay_size,np.alen(tY) ))) tX = tX[train_A,:] tY = tY[train_A,:] tW = tW[train_A,:] noisy_model.fit(tX,tY, batch_size=mini_batch, nb_epoch=training_epochs,verbose=0)
Example #27
Source File: BipedalWalker_v3.0.py From FitML with MIT License | 5 votes |
def train_noisy_actor(): tX = (memoryS) tY = (memoryA) tW = (memoryW) train_A = np.random.randint(tY.shape[0],size=int(min(experience_replay_size,np.alen(tY) ))) tX = tX[train_A,:] tY = tY[train_A,:] tW = tW[train_A,:] noisy_model.fit(tX,tY, batch_size=mini_batch, nb_epoch=training_epochs,verbose=0)
Example #28
Source File: BipedalWalker_v3.0.py From FitML with MIT License | 5 votes |
def reset_noisy_model(): sz = len(noisy_model.layers) #if largeNoise: # print("Setting Large Noise!") for k in range(sz): w = noisy_model.layers[k].get_weights() apW = action_predictor_model.layers[k].get_weights() if np.alen(w) >0: w[0] = reset_noisy_model_weights_to_apWeights(apW[0]) noisy_model.layers[k].set_weights(w) #print("w",w) #print("apW",apW)
Example #29
Source File: HalfCheetah.v1.0.py From FitML with MIT License | 5 votes |
def add_controlled_noise(largeNoise = False): tR = (memoryR) tX = (memoryS) tY = (memoryA) tW = (memoryW) train_C = np.random.randint(tY.shape[0],size=100) tX = tX[train_C,:] tY_old = tY[train_C,:] tY_new = tY[train_C,:] diffs = np.zeros(np.alen(tX)) delta = 1000 deltaCount = 0 while delta > 1: noisy_model.set_weights(action_predictor_model.get_weights()) add_noise_to_model(True) for i in range(np.alen(tX)): a = GetRememberedOptimalPolicy(tX[i]) b = GetRememberedOptimalPolicyFromNoisyModel(tX[i]) a = a.flatten() b = b.flatten() c = np.abs(a-b) diffs[i] = c.mean() delta = np.average (diffs) deltaCount+=1 print("Tried x time ", deltaCount,"delta =", delta) #Play the game 500 times
Example #30
Source File: HalfCheetah.v1.0.py From FitML with MIT License | 5 votes |
def train_noisy_actor(): tX = (memoryS) tY = (memoryA) tW = (memoryW) train_A = np.random.randint(tY.shape[0],size=int(min(experience_replay_size,np.alen(tY) ))) tX = tX[train_A,:] tY = tY[train_A,:] tW = tW[train_A,:] noisy_model.fit(tX,tY, batch_size=mini_batch, nb_epoch=training_epochs,verbose=0)