3/22/2023 0 Comments Tf permute![]() Layer):ĭef _init_( self, c1, c2, shortcut = True, g = 1, e = 0.5, w = None): # ch_in, ch_out, shortcut, groups, expansion concat( inputs, 3))Ĭlass TFBottleneck( keras. # inputs = inputs / 255 # normalize 0-255 to 0-1 conv = TFConv( c1 * 4, c2, k, s, p, g, act, w. ![]() # ch_in, ch_out, kernel, stride, padding, groups Constant( weight),īias_initializer = keras. conv( inputs)))ĭef _init_( self, c1, c2, k = 1, s = 1, p = None, act = True, w = None):Īssert c2 % c1 = 0, f'TFDWConv() output= channels'Īssert k = 4 and p1 = 1, 'TFDWConv() only valid for k=4 and p1=1' numpy()),īias_initializer = 'zeros' if hasattr( w, 'bn') else keras. # TensorFlow convolution padding is inconsistent with PyTorch (e.g. # ch_in, ch_out, weights, kernel, stride, padding, groupsĪssert g = 1, "TF v2.2 Conv2D does not support 'groups' argument" pad, mode = 'constant', constant_values = 0)ĭef _init_( self, c1, c2, k = 1, s = 1, p = None, g = 1, act = True, w = None): # Pad inputs in spatial dimensions 1 and 2 BatchNormalization(īeta_initializer = keras. general import LOGGER, make_divisible, print_args experimental import MixConv2d, attempt_loadįrom utils. common import ( C3, SPP, SPPF, Bottleneck, BottleneckCSP, C3x, Concat, Conv, CrossConv, DWConv,įrom models. # ROOT = ROOT.relative_to(Path.cwd()) # relativeįrom models. $ python export.py -weights yolov5s.pt -include saved_model pb tflite tfjs $ python models/tf.py -weights yolov5s.pt TensorFlow, Keras and TFLite versions of YOLOv5 ![]() ![]() # YOLOv5 □ by Ultralytics, GPL-3.0 license dqn.TFBN Class _init_ Function call Function TFPad Class _init_ Function call Function TFConv Class _init_ Function call Function TFDWConv Class _init_ Function call Function TFDWConvTranspose2d Class _init_ Function call Function TFFocus Class _init_ Function call Function TFBottleneck Class _init_ Function call Function TFCrossConv Class _init_ Function call Function TFConv2d Class _init_ Function call Function TFBottleneckCSP Class _init_ Function call Function TFC3 Class _init_ Function call Function TFC3x Class _init_ Function call Function TFSPP Class _init_ Function call Function TFSPPF Class _init_ Function call Function TFDetect Class _init_ Function call Function _make_grid Function TFSegment Class _init_ Function call Function TFProto Class _init_ Function call Function TFUpsample Class _init_ Function call Function TFConcat Class _init_ Function call Function parse_model Function TFModel Class _init_ Function predict Function _xywh2xyxy Function AgnosticNMS Class call Function _nms Function activations Function representative_dataset_gen Function run Function parse_opt Function main Function #dqn.fit(env, callbacks=callbacks, nb_steps=1000, log_interval=100) # After training is done, we save the final weights one more time. fit( env, callbacks= callbacks, nb_steps= 5750000, log_interval= 50000) load_weights( dqn_Pong-v0_weights_final.h5f)ĭqn. format( env_name)Ĭallbacks = Ĭallbacks += ĭqn. Notice that you can use the built-in Keras callbacks! results_path = '/results/' weights_filename = results_path + 'dqn_log.json'. # Okay, now it's time to learn something! We capture the interrupt exception so that training # can be prematurely aborted. Memory= memory, processor= processor, nb_steps_warmup= 10,ĭqn. dqn = DQNAgent( model= model, nb_actions= nb_actions, policy= policy, ![]() # Creating the DQN agent with the specified model, policy, memory etc. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |