Fix badness

Author: pratikgujjar

code/autoencoder_model/scripts/.res_16.py.swp

@@ -31,7 +31,7 @@
 TEST_DATA_DIR= '/local_home/JAAD_Dataset/iros/resized_imgs_208_thesis/test/'
 # TEST_DATA_DIR= '/local_home/JAAD_Dataset/fun_experiments/resized/'
 
-RESULTS_DIR = '/local_home/JAAD_Dataset/thesis/results/DRRK16/'
+RESULTS_DIR = '/local_home/JAAD_Dataset/thesis/results/res/'
 
 # MODEL_DIR = './../' + path_var + 'models'
 MODEL_DIR = RESULTS_DIR + 'models/'
@@ -77,7 +77,7 @@
 print ("Config file: " + str(__name__))
 
 BATCH_SIZE = 9
-TEST_BATCH_SIZE = 1
+TEST_BATCH_SIZE = 9
 NB_EPOCHS_AUTOENCODER = 30
 
 # OPTIM_A = Adam(lr=0.0001, beta_1=0.5)

code/autoencoder_model/scripts/thesis_scripts/config_r16.py

@@ -0,0 +1,102 @@
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from keras.optimizers import SGD
+from keras.optimizers import Adam
+from keras.optimizers import adadelta
+from keras.optimizers import rmsprop
+from keras.layers import Layer
+from keras import backend as K
+K.set_image_dim_ordering('tf')
+import socket
+import os
+
+# -------------------------------------------------
+# Background config:
+hostname = socket.gethostname()
+if hostname == 'baymax':
+    path_var = 'baymax/'
+elif hostname == 'walle':
+    path_var = 'walle/'
+elif hostname == 'bender':
+    path_var = 'bender/'
+else:
+    path_var = 'zhora/'
+
+DATA_DIR= '/local_home/JAAD_Dataset/iros/resized_imgs_208_thesis/train/'
+
+VAL_DATA_DIR= '/local_home/JAAD_Dataset/iros/resized_imgs_208_thesis/val/'
+
+TEST_DATA_DIR= '/local_home/JAAD_Dataset/iros/resized_imgs_208_thesis/test/'
+# TEST_DATA_DIR= '/local_home/JAAD_Dataset/fun_experiments/resized/'
+
+RESULTS_DIR = '/local_home/JAAD_Dataset/thesis/results/NRNN16/'
+
+# MODEL_DIR = './../' + path_var + 'models'
+MODEL_DIR = RESULTS_DIR + 'models/'
+if not os.path.exists(MODEL_DIR):
+    os.mkdir(MODEL_DIR)
+
+# CHECKPOINT_DIR = './../' + path_var + 'checkpoints'
+CHECKPOINT_DIR = RESULTS_DIR + 'checkpoints/'
+if not os.path.exists(CHECKPOINT_DIR):
+    os.mkdir(CHECKPOINT_DIR)
+
+GEN_IMAGES_DIR = RESULTS_DIR + 'generated_images/'
+if not os.path.exists(GEN_IMAGES_DIR):
+    os.mkdir(GEN_IMAGES_DIR)
+
+LOG_DIR = RESULTS_DIR + 'logs/'
+if not os.path.exists(LOG_DIR):
+    os.mkdir(LOG_DIR)
+
+TF_LOG_DIR = RESULTS_DIR + 'tf_logs/'
+if not os.path.exists(TF_LOG_DIR):
+    os.mkdir(TF_LOG_DIR)
+
+TEST_RESULTS_DIR = RESULTS_DIR + 'test_results/'
+if not os.path.exists(TEST_RESULTS_DIR):
+    os.mkdir(TEST_RESULTS_DIR)
+
+PRINT_MODEL_SUMMARY = True
+SAVE_MODEL = True
+PLOT_MODEL = True
+SAVE_GENERATED_IMAGES = True
+SHUFFLE = True
+VIDEO_LENGTH = 32
+IMG_SIZE = (128, 208, 3)
+RAM_DECIMATE = False
+REVERSE = True
+FILTER_SIZE = 3
+
+
+# -------------------------------------------------
+# Network configuration:
+print ("Loading network/training configuration.")
+print ("Config file: " + str(__name__))
+
+BATCH_SIZE = 9
+TEST_BATCH_SIZE = 1
+NB_EPOCHS_AUTOENCODER = 30
+
+# OPTIM_A = Adam(lr=0.0001, beta_1=0.5)
+OPTIM_A = rmsprop(lr=0.0001, rho=0.9)
+OPTIM_B = rmsprop(lr=0.00001, rho=0.9)
+# OPTIM_A = SGD(lr=0.000001, momentum=0.5, nesterov=True)
+
+lr_schedule = [7, 14, 20, 30]  # epoch_step
+
+def schedule(epoch_idx):
+    if (epoch_idx) <= lr_schedule[0]:
+        return 0.001
+    elif (epoch_idx) <= lr_schedule[1]:
+        return 0.0001  # lr_decay_ratio = 10
+    elif (epoch_idx) <= lr_schedule[2]:
+        return 0.00001  # lr_decay_ratio = 10
+    elif (epoch_idx) <= lr_schedule[3]:
+        return 0.00001
+    return 0.00001
+
+
+