Mask R-CNN - Inspect Weights of a Trained Model

This notebook includes code and visualizations to test, debug, and evaluate the Mask R-CNN model.

import os
import sys
import numpy as np
import tensorflow as tf
import matplotlib
import matplotlib.pyplot as plt
import keras

# Root directory of the project
ROOT_DIR = os.path.abspath("../../")

# Import Mask RCNN
sys.path.append(ROOT_DIR)  # To find local version of the library
from mrcnn import utils
import mrcnn.model as modellib
from mrcnn import visualize
from mrcnn.model import log

%matplotlib inline 

# Directory to save logs and trained model
MODEL_DIR = os.path.join(ROOT_DIR, "logs")

# Local path to trained weights file
COCO_MODEL_PATH = os.path.join(ROOT_DIR, "mask_rcnn_coco.h5")
# Download COCO trained weights from Releases if needed
if not os.path.exists(COCO_MODEL_PATH):
    utils.download_trained_weights(COCO_MODEL_PATH)

# Path to Shapes trained weights
SHAPES_MODEL_PATH = os.path.join(ROOT_DIR, "mask_rcnn_shapes.h5")
Using TensorFlow backend.

Configurations

# Run one of the code blocks

# Shapes toy dataset
# import shapes
# config = shapes.ShapesConfig()

# MS COCO Dataset
import coco
config = coco.CocoConfig()

Notebook Preferences

# Device to load the neural network on.
# Useful if you're training a model on the same 
# machine, in which case use CPU and leave the
# GPU for training.
DEVICE = "/cpu:0"  # /cpu:0 or /gpu:0
def get_ax(rows=1, cols=1, size=16):
    """Return a Matplotlib Axes array to be used in
    all visualizations in the notebook. Provide a
    central point to control graph sizes.
    
    Adjust the size attribute to control how big to render images
    """
    _, ax = plt.subplots(rows, cols, figsize=(size*cols, size*rows))
    return ax

Load Model

# Create model in inference mode
with tf.device(DEVICE):
    model = modellib.MaskRCNN(mode="inference", model_dir=MODEL_DIR,
                              config=config)

# Set weights file path
if config.NAME == "shapes":
    weights_path = SHAPES_MODEL_PATH
elif config.NAME == "coco":
    weights_path = COCO_MODEL_PATH
# Or, uncomment to load the last model you trained
# weights_path = model.find_last()

# Load weights
print("Loading weights ", weights_path)
model.load_weights(weights_path, by_name=True)

Review Weight Stats

# Show stats of all trainable weights    
visualize.display_weight_stats(model)
conv1/kernel:0 (7, 7, 3, 64) -0.8616 +0.8539 +0.1314
bn_conv1/gamma:0 (64,) +0.0843 +2.6420 +0.5087
bn_conv1/beta:0 (64,) -2.4174 +5.4189 +1.9981
bn_conv1/moving_mean:0 (64,) -172.9685 +94.5717 +42.0063
bn_conv1/moving_variance:0*** Overflow? (64,) +0.0000 +110557.9688 +16228.7607
res2a_branch2a/kernel:0 (1, 1, 64, 64) -0.6603 +0.3208 +0.0768
bn2a_branch2a/gamma:0 (64,) +0.2189 +1.8654 +0.4149
bn2a_branch2a/beta:0 (64,) -2.1375 +3.7690 +1.1904
bn2a_branch2a/moving_mean:0 (64,) -6.3118 +7.4370 +2.4037
bn2a_branch2a/moving_variance:0 (64,) +0.0000 +8.8091 +2.1498
res2a_branch2b/kernel:0 (3, 3, 64, 64) -0.3813 +0.5123 +0.0323
bn2a_branch2b/gamma:0 (64,) +0.3195 +1.7454 +0.3143
bn2a_branch2b/beta:0 (64,) -1.9530 +4.5882 +1.5261
bn2a_branch2b/moving_mean:0 (64,) -6.7890 +4.2754 +2.2064
bn2a_branch2b/moving_variance:0 (64,) +0.0000 +5.5464 +1.1573
res2a_branch2c/kernel:0 (1, 1, 64, 256) -0.4412 +0.3600 +0.0411
res2a_branch1/kernel:0 (1, 1, 64, 256) -0.8513 +0.7543 +0.0699
bn2a_branch2c/gamma:0 (256,) -0.5887 +3.2101 +0.6259
bn2a_branch2c/beta:0 (256,) -1.1511 +1.4415 +0.4269
bn2a_branch2c/moving_mean:0 (256,) -4.2796 +3.1055 +1.0352
bn2a_branch2c/moving_variance:0 (256,) +0.0000 +2.6966 +0.4085
bn2a_branch1/gamma:0 (256,) +0.2415 +3.5354 +0.6298
bn2a_branch1/beta:0 (256,) -1.1511 +1.4415 +0.4269
bn2a_branch1/moving_mean:0 (256,) -8.1191 +8.7749 +2.0398
bn2a_branch1/moving_variance:0 (256,) +0.0000 +10.3201 +1.6540
res2b_branch2a/kernel:0 (1, 1, 256, 64) -0.2418 +0.2263 +0.0358
bn2b_branch2a/gamma:0 (64,) +0.2051 +1.7890 +0.3852
bn2b_branch2a/beta:0 (64,) -2.0730 +1.6836 +0.8930
bn2b_branch2a/moving_mean:0 (64,) -1.8157 +1.7829 +0.7466
bn2b_branch2a/moving_variance:0 (64,) +0.0000 +3.2496 +0.7830
res2b_branch2b/kernel:0 (3, 3, 64, 64) -0.5190 +0.3431 +0.0357
bn2b_branch2b/gamma:0 (64,) +0.5190 +1.4828 +0.2283
bn2b_branch2b/beta:0 (64,) -2.4756 +2.7818 +1.2069
bn2b_branch2b/moving_mean:0 (64,) -1.8361 +0.9368 +0.5723
bn2b_branch2b/moving_variance:0 (64,) +0.0938 +1.0783 +0.2077
res2b_branch2c/kernel:0 (1, 1, 64, 256) -0.3330 +0.3228 +0.0414
bn2b_branch2c/gamma:0 (256,) -0.0329 +1.8095 +0.4257
bn2b_branch2c/beta:0 (256,) -1.3059 +0.9721 +0.3463
bn2b_branch2c/moving_mean:0 (256,) -2.5336 +2.1111 +0.5033
bn2b_branch2c/moving_variance:0 (256,) +0.0000 +0.2187 +0.0333
res2c_branch2a/kernel:0 (1, 1, 256, 64) -0.3040 +0.2175 +0.0412
bn2c_branch2a/gamma:0 (64,) +0.2683 +1.8338 +0.2863
bn2c_branch2a/beta:0 (64,) -2.0358 +0.8512 +0.7946
bn2c_branch2a/moving_mean:0 (64,) -4.7340 +1.6664 +1.2255
bn2c_branch2a/moving_variance:0 (64,) +0.0000 +3.4985 +0.7644
res2c_branch2b/kernel:0 (3, 3, 64, 64) -0.2020 +0.2138 +0.0378
bn2c_branch2b/gamma:0 (64,) +0.6155 +1.5482 +0.2177
bn2c_branch2b/beta:0 (64,) -2.4321 +1.8318 +0.6352
bn2c_branch2b/moving_mean:0 (64,) -1.4939 +0.1422 +0.2628
bn2c_branch2b/moving_variance:0 (64,) +0.2278 +2.0831 +0.3304
res2c_branch2c/kernel:0 (1, 1, 64, 256) -0.2842 +0.2529 +0.0430
bn2c_branch2c/gamma:0 (256,) -0.0200 +2.3871 +0.5297
bn2c_branch2c/beta:0 (256,) -1.6989 +1.1085 +0.4321
bn2c_branch2c/moving_mean:0 (256,) -1.2794 +0.7256 +0.2929
bn2c_branch2c/moving_variance:0 (256,) +0.0010 +0.7414 +0.1125
res3a_branch2a/kernel:0 (1, 1, 256, 128) -0.5027 +0.6187 +0.0304
bn3a_branch2a/gamma:0 (128,) +0.4905 +1.3262 +0.1895
bn3a_branch2a/beta:0 (128,) -1.8565 +2.5853 +0.7641
bn3a_branch2a/moving_mean:0 (128,) -4.2267 +2.1703 +0.9005
bn3a_branch2a/moving_variance:0 (128,) +0.0545 +8.9011 +1.2696
res3a_branch2b/kernel:0 (3, 3, 128, 128) -0.3236 +0.4518 +0.0223
bn3a_branch2b/gamma:0 (128,) +0.4706 +1.8430 +0.2200
bn3a_branch2b/beta:0 (128,) -1.9615 +1.9157 +0.7933
bn3a_branch2b/moving_mean:0 (128,) -6.0335 +3.2213 +1.9778
bn3a_branch2b/moving_variance:0 (128,) +0.0001 +6.6136 +0.8450
res3a_branch2c/kernel:0 (1, 1, 128, 512) -0.4927 +0.3402 +0.0283
res3a_branch1/kernel:0 (1, 1, 256, 512) -0.4507 +0.6643 +0.0290
bn3a_branch2c/gamma:0 (512,) -0.0033 +3.7310 +0.6223
bn3a_branch2c/beta:0 (512,) -0.9694 +1.4581 +0.3727
bn3a_branch2c/moving_mean:0 (512,) -1.5891 +1.4301 +0.3907
bn3a_branch2c/moving_variance:0 (512,) +0.0002 +0.8632 +0.1073
bn3a_branch1/gamma:0 (512,) -0.0138 +2.7207 +0.4812
bn3a_branch1/beta:0 (512,) -0.9694 +1.4580 +0.3727
bn3a_branch1/moving_mean:0 (512,) -3.7382 +2.8448 +0.8043
bn3a_branch1/moving_variance:0 (512,) +0.0029 +5.7492 +0.6063
res3b_branch2a/kernel:0 (1, 1, 512, 128) -0.1992 +0.1954 +0.0252
bn3b_branch2a/gamma:0 (128,) +0.5946 +1.5472 +0.1807
bn3b_branch2a/beta:0 (128,) -3.9918 +0.6877 +0.6492
bn3b_branch2a/moving_mean:0 (128,) -3.0865 +1.0082 +0.6388
bn3b_branch2a/moving_variance:0 (128,) +0.2999 +3.6467 +0.6298
res3b_branch2b/kernel:0 (3, 3, 128, 128) -0.2305 +0.2760 +0.0241
bn3b_branch2b/gamma:0 (128,) +0.4863 +1.5054 +0.2365
bn3b_branch2b/beta:0 (128,) -2.4436 +1.4355 +0.6833
bn3b_branch2b/moving_mean:0 (128,) -2.1539 +1.4636 +0.5130
bn3b_branch2b/moving_variance:0 (128,) +0.0961 +1.6067 +0.2474
res3b_branch2c/kernel:0 (1, 1, 128, 512) -0.3111 +0.4652 +0.0288
bn3b_branch2c/gamma:0 (512,) -0.0367 +2.0281 +0.4083
bn3b_branch2c/beta:0 (512,) -1.5916 +1.1728 +0.3779
bn3b_branch2c/moving_mean:0 (512,) -1.0827 +0.6886 +0.2301
bn3b_branch2c/moving_variance:0 (512,) +0.0002 +0.2121 +0.0303
res3c_branch2a/kernel:0 (1, 1, 512, 128) -0.2663 +0.2643 +0.0284
bn3c_branch2a/gamma:0 (128,) +0.5964 +1.5047 +0.1916
bn3c_branch2a/beta:0 (128,) -2.8335 +0.7305 +0.5454
bn3c_branch2a/moving_mean:0 (128,) -3.0245 +1.7478 +0.8335
bn3c_branch2a/moving_variance:0 (128,) +0.2705 +3.8971 +0.7585
res3c_branch2b/kernel:0 (3, 3, 128, 128) -0.2296 +0.1968 +0.0233
bn3c_branch2b/gamma:0 (128,) +0.4471 +1.5901 +0.2878
bn3c_branch2b/beta:0 (128,) -1.4295 +1.3293 +0.5597
bn3c_branch2b/moving_mean:0 (128,) -1.0368 +0.7158 +0.2785
bn3c_branch2b/moving_variance:0 (128,) +0.1252 +0.9441 +0.1439
res3c_branch2c/kernel:0 (1, 1, 128, 512) -0.3068 +0.3730 +0.0263
bn3c_branch2c/gamma:0 (512,) -0.0337 +1.9127 +0.3803
bn3c_branch2c/beta:0 (512,) -1.5568 +0.8346 +0.3645
bn3c_branch2c/moving_mean:0 (512,) -0.8692 +0.7091 +0.1843
bn3c_branch2c/moving_variance:0 (512,) +0.0002 +0.1747 +0.0270
res3d_branch2a/kernel:0 (1, 1, 512, 128) -0.2611 +0.2868 +0.0307
bn3d_branch2a/gamma:0 (128,) +0.6513 +1.4620 +0.1939
bn3d_branch2a/beta:0 (128,) -3.0044 +0.6187 +0.6454
bn3d_branch2a/moving_mean:0 (128,) -3.7891 +2.2792 +0.9724
bn3d_branch2a/moving_variance:0 (128,) +0.0035 +3.3270 +0.5634
res3d_branch2b/kernel:0 (3, 3, 128, 128) -0.1610 +0.2406 +0.0237
bn3d_branch2b/gamma:0 (128,) +0.6456 +3.2960 +0.2932
bn3d_branch2b/beta:0 (128,) -1.6672 +1.5725 +0.5648
bn3d_branch2b/moving_mean:0 (128,) -1.0483 +0.3162 +0.2877
bn3d_branch2b/moving_variance:0 (128,) +0.2354 +1.6038 +0.2114
res3d_branch2c/kernel:0 (1, 1, 128, 512) -0.2424 +0.3360 +0.0271
bn3d_branch2c/gamma:0 (512,) -0.0229 +1.9388 +0.5210
bn3d_branch2c/beta:0 (512,) -1.0656 +0.9844 +0.2739
bn3d_branch2c/moving_mean:0 (512,) -1.1224 +0.4736 +0.2424
bn3d_branch2c/moving_variance:0 (512,) +0.0002 +0.4452 +0.0605
res4a_branch2a/kernel:0 (1, 1, 512, 256) -0.2850 +0.2532 +0.0148
bn4a_branch2a/gamma:0 (256,) +0.4807 +1.4740 +0.1509
bn4a_branch2a/beta:0 (256,) -1.9598 +1.1296 +0.3918
bn4a_branch2a/moving_mean:0 (256,) -3.6537 +1.2955 +0.6511
bn4a_branch2a/moving_variance:0 (256,) +0.0614 +2.4605 +0.3051
res4a_branch2b/kernel:0 (3, 3, 256, 256) -0.1724 +0.1962 +0.0106
bn4a_branch2b/gamma:0 (256,) +0.4749 +1.5932 +0.2005
bn4a_branch2b/beta:0 (256,) -2.6223 +1.1253 +0.4850
bn4a_branch2b/moving_mean:0 (256,) -2.6573 +3.4857 +0.5984
bn4a_branch2b/moving_variance:0 (256,) +0.1394 +1.4291 +0.2191
res4a_branch2c/kernel:0 (1, 1, 256, 1024) -0.2826 +0.1895 +0.0140
res4a_branch1/kernel:0 (1, 1, 512, 1024) -0.3567 +0.3337 +0.0158
bn4a_branch2c/gamma:0 (1024,) -0.0100 +2.8427 +0.4587
bn4a_branch2c/beta:0 (1024,) -0.5290 +2.0636 +0.2888
bn4a_branch2c/moving_mean:0 (1024,) -0.4885 +0.2798 +0.0831
bn4a_branch2c/moving_variance:0 (1024,) +0.0000 +0.1206 +0.0119
bn4a_branch1/gamma:0 (1024,) +0.1721 +4.0220 +0.7190
bn4a_branch1/beta:0 (1024,) -0.5290 +2.0638 +0.2888
bn4a_branch1/moving_mean:0 (1024,) -5.8239 +3.2843 +0.9039
bn4a_branch1/moving_variance:0 (1024,) +0.0546 +8.5205 +0.6964
res4b_branch2a/kernel:0 (1, 1, 1024, 256) -0.1260 +0.1778 +0.0081
bn4b_branch2a/gamma:0 (256,) +0.4198 +1.6067 +0.1885
bn4b_branch2a/beta:0 (256,) -2.2254 +2.0613 +0.4898
bn4b_branch2a/moving_mean:0 (256,) -4.6812 +2.7681 +1.1843
bn4b_branch2a/moving_variance:0 (256,) +0.1327 +11.9923 +1.3851
res4b_branch2b/kernel:0 (3, 3, 256, 256) -0.0965 +0.1632 +0.0072
bn4b_branch2b/gamma:0 (256,) +0.5049 +1.4772 +0.1899
bn4b_branch2b/beta:0 (256,) -1.6971 +0.5928 +0.4359
bn4b_branch2b/moving_mean:0 (256,) -5.7095 +1.9171 +0.8444
bn4b_branch2b/moving_variance:0 (256,) +0.0203 +1.3132 +0.2172
res4b_branch2c/kernel:0 (1, 1, 256, 1024) -0.1962 +0.2793 +0.0103
bn4b_branch2c/gamma:0 (1024,) -0.0006 +3.1835 +0.3656
bn4b_branch2c/beta:0 (1024,) -1.0684 +0.9525 +0.1818
bn4b_branch2c/moving_mean:0 (1024,) -0.4443 +0.4752 +0.0943
bn4b_branch2c/moving_variance:0 (1024,) +0.0000 +0.1888 +0.0161
res4c_branch2a/kernel:0 (1, 1, 1024, 256) -0.0990 +0.1245 +0.0082
bn4c_branch2a/gamma:0 (256,) +0.5761 +1.7694 +0.1421
bn4c_branch2a/beta:0 (256,) -0.9332 +1.3302 +0.3766
bn4c_branch2a/moving_mean:0 (256,) -4.0562 +2.6203 +1.2418
bn4c_branch2a/moving_variance:0 (256,) +0.3526 +5.5460 +0.6766
res4c_branch2b/kernel:0 (3, 3, 256, 256) -0.0971 +0.1085 +0.0074
bn4c_branch2b/gamma:0 (256,) +0.5058 +1.2290 +0.1501
bn4c_branch2b/beta:0 (256,) -1.4553 +0.5811 +0.3399
bn4c_branch2b/moving_mean:0 (256,) -3.4174 +1.7898 +0.5966
bn4c_branch2b/moving_variance:0 (256,) +0.0730 +1.5945 +0.1712
res4c_branch2c/kernel:0 (1, 1, 256, 1024) -0.1343 +0.1620 +0.0107
bn4c_branch2c/gamma:0 (1024,) +0.0047 +2.2900 +0.2671
bn4c_branch2c/beta:0 (1024,) -1.1121 +0.7399 +0.1807
bn4c_branch2c/moving_mean:0 (1024,) -0.3355 +0.1449 +0.0609
bn4c_branch2c/moving_variance:0 (1024,) +0.0003 +0.0969 +0.0076
res4d_branch2a/kernel:0 (1, 1, 1024, 256) -0.1154 +0.1464 +0.0103
bn4d_branch2a/gamma:0 (256,) +0.5715 +1.4526 +0.1505
bn4d_branch2a/beta:0 (256,) -1.3573 +0.4802 +0.3064
bn4d_branch2a/moving_mean:0 (256,) -3.2371 +2.2156 +0.9378
bn4d_branch2a/moving_variance:0 (256,) +0.3515 +5.9965 +0.7573
res4d_branch2b/kernel:0 (3, 3, 256, 256) -0.0995 +0.0993 +0.0087
bn4d_branch2b/gamma:0 (256,) +0.4295 +1.5526 +0.1616
bn4d_branch2b/beta:0 (256,) -1.4477 +0.3874 +0.2842
bn4d_branch2b/moving_mean:0 (256,) -1.4094 +0.7436 +0.2812
bn4d_branch2b/moving_variance:0 (256,) +0.0523 +0.4509 +0.0769
res4d_branch2c/kernel:0 (1, 1, 256, 1024) -0.2408 +0.1240 +0.0118
bn4d_branch2c/gamma:0 (1024,) +0.0455 +2.8707 +0.3316
bn4d_branch2c/beta:0 (1024,) -1.3654 +0.5976 +0.2313
bn4d_branch2c/moving_mean:0 (1024,) -0.3255 +0.1030 +0.0535
bn4d_branch2c/moving_variance:0 (1024,) +0.0017 +0.1031 +0.0077
res4e_branch2a/kernel:0 (1, 1, 1024, 256) -0.1464 +0.1125 +0.0101
bn4e_branch2a/gamma:0 (256,) +0.6457 +1.3797 +0.1243
bn4e_branch2a/beta:0 (256,) -1.0967 +0.3581 +0.2712
bn4e_branch2a/moving_mean:0 (256,) -4.3375 +1.8791 +1.1414
bn4e_branch2a/moving_variance:0 (256,) +0.3798 +4.6768 +0.6343
res4e_branch2b/kernel:0 (3, 3, 256, 256) -0.0862 +0.0903 +0.0090
bn4e_branch2b/gamma:0 (256,) +0.5497 +1.2982 +0.1328
bn4e_branch2b/beta:0 (256,) -1.2916 +0.2223 +0.2549
bn4e_branch2b/moving_mean:0 (256,) -1.6195 +0.8106 +0.3150
bn4e_branch2b/moving_variance:0 (256,) +0.0594 +0.9789 +0.1007
res4e_branch2c/kernel:0 (1, 1, 256, 1024) -0.2030 +0.1746 +0.0119
bn4e_branch2c/gamma:0 (1024,) +0.0241 +1.7471 +0.1844
bn4e_branch2c/beta:0 (1024,) -1.0605 +0.4113 +0.1782
bn4e_branch2c/moving_mean:0 (1024,) -0.2640 +0.1132 +0.0421
bn4e_branch2c/moving_variance:0 (1024,) +0.0009 +0.0492 +0.0041
res4f_branch2a/kernel:0 (1, 1, 1024, 256) -0.0842 +0.1227 +0.0105
bn4f_branch2a/gamma:0 (256,) +0.7024 +1.3934 +0.1006
bn4f_branch2a/beta:0 (256,) -1.1660 +0.3933 +0.2485
bn4f_branch2a/moving_mean:0 (256,) -4.4642 +2.1927 +1.1421
bn4f_branch2a/moving_variance:0 (256,) +0.4538 +4.7501 +0.7294
res4f_branch2b/kernel:0 (3, 3, 256, 256) -0.1032 +0.0997 +0.0095
bn4f_branch2b/gamma:0 (256,) +0.4258 +1.2521 +0.1110
bn4f_branch2b/beta:0 (256,) -1.4425 +0.5213 +0.2464
bn4f_branch2b/moving_mean:0 (256,) -1.8044 +1.5505 +0.3778
bn4f_branch2b/moving_variance:0 (256,) +0.0635 +0.6795 +0.0933
res4f_branch2c/kernel:0 (1, 1, 256, 1024) -0.1423 +0.1278 +0.0119
bn4f_branch2c/gamma:0 (1024,) +0.1776 +1.6305 +0.1749
bn4f_branch2c/beta:0 (1024,) -0.9891 +0.3002 +0.1423
bn4f_branch2c/moving_mean:0 (1024,) -0.1586 +0.0747 +0.0354
bn4f_branch2c/moving_variance:0 (1024,) +0.0012 +0.0210 +0.0024
res4g_branch2a/kernel:0 (1, 1, 1024, 256) -0.1148 +0.2416 +0.0107
bn4g_branch2a/gamma:0 (256,) +0.6074 +1.2107 +0.1073
bn4g_branch2a/beta:0 (256,) -1.2790 +0.2364 +0.2842
bn4g_branch2a/moving_mean:0 (256,) -4.3445 +1.4500 +1.0834
bn4g_branch2a/moving_variance:0 (256,) +0.3768 +3.8029 +0.7079
res4g_branch2b/kernel:0 (3, 3, 256, 256) -0.1280 +0.1199 +0.0097
bn4g_branch2b/gamma:0 (256,) +0.4760 +1.7497 +0.1351
bn4g_branch2b/beta:0 (256,) -1.2725 +0.1908 +0.2716
bn4g_branch2b/moving_mean:0 (256,) -1.1725 +1.0331 +0.2961
bn4g_branch2b/moving_variance:0 (256,) +0.0579 +0.7416 +0.0856
res4g_branch2c/kernel:0 (1, 1, 256, 1024) -0.1498 +0.2285 +0.0118
bn4g_branch2c/gamma:0 (1024,) +0.0908 +1.8260 +0.1987
bn4g_branch2c/beta:0 (1024,) -0.9102 +0.2949 +0.1424
bn4g_branch2c/moving_mean:0 (1024,) -0.1887 +0.0784 +0.0394
bn4g_branch2c/moving_variance:0 (1024,) +0.0013 +0.0316 +0.0033
res4h_branch2a/kernel:0 (1, 1, 1024, 256) -0.1305 +0.1624 +0.0116
bn4h_branch2a/gamma:0 (256,) +0.6257 +1.2189 +0.0991
bn4h_branch2a/beta:0 (256,) -1.4250 +0.0732 +0.2633
bn4h_branch2a/moving_mean:0 (256,) -3.7871 +2.4121 +0.9370
bn4h_branch2a/moving_variance:0 (256,) +0.5296 +3.3483 +0.5827
res4h_branch2b/kernel:0 (3, 3, 256, 256) -0.0986 +0.1224 +0.0102
bn4h_branch2b/gamma:0 (256,) +0.4840 +1.4915 +0.1486
bn4h_branch2b/beta:0 (256,) -1.5969 +0.4351 +0.2542
bn4h_branch2b/moving_mean:0 (256,) -1.0446 +1.1061 +0.2066
bn4h_branch2b/moving_variance:0 (256,) +0.0492 +0.5152 +0.0675
res4h_branch2c/kernel:0 (1, 1, 256, 1024) -0.1463 +0.2364 +0.0120
bn4h_branch2c/gamma:0 (1024,) +0.0535 +2.4894 +0.2982
bn4h_branch2c/beta:0 (1024,) -0.8032 +0.3345 +0.1533
bn4h_branch2c/moving_mean:0 (1024,) -0.2073 +0.1215 +0.0407
bn4h_branch2c/moving_variance:0 (1024,) +0.0012 +0.0485 +0.0043
res4i_branch2a/kernel:0 (1, 1, 1024, 256) -0.1310 +0.2966 +0.0130
bn4i_branch2a/gamma:0 (256,) +0.3535 +1.0589 +0.1278
bn4i_branch2a/beta:0 (256,) -1.4845 +0.4568 +0.2795
bn4i_branch2a/moving_mean:0 (256,) -4.1849 +2.5485 +1.1041
bn4i_branch2a/moving_variance:0 (256,) +0.7884 +7.4350 +0.6603
res4i_branch2b/kernel:0 (3, 3, 256, 256) -0.1289 +0.1534 +0.0092
bn4i_branch2b/gamma:0 (256,) +0.5686 +1.6406 +0.1422
bn4i_branch2b/beta:0 (256,) -1.4987 +0.4807 +0.2660
bn4i_branch2b/moving_mean:0 (256,) -0.5127 +0.1199 +0.0935
bn4i_branch2b/moving_variance:0 (256,) +0.0143 +0.1193 +0.0159
res4i_branch2c/kernel:0 (1, 1, 256, 1024) -0.1553 +0.1522 +0.0118
bn4i_branch2c/gamma:0 (1024,) +0.0405 +2.1795 +0.1866
bn4i_branch2c/beta:0 (1024,) -0.5968 +0.7084 +0.1265
bn4i_branch2c/moving_mean:0 (1024,) -0.4519 +0.1328 +0.0659
bn4i_branch2c/moving_variance:0 (1024,) +0.0008 +0.0992 +0.0064
res4j_branch2a/kernel:0 (1, 1, 1024, 256) -0.1212 +0.1714 +0.0124
bn4j_branch2a/gamma:0 (256,) +0.5074 +1.2941 +0.1164
bn4j_branch2a/beta:0 (256,) -1.9232 +0.2171 +0.2896
bn4j_branch2a/moving_mean:0 (256,) -4.7605 +1.3667 +0.9790
bn4j_branch2a/moving_variance:0 (256,) +0.8111 +8.3345 +0.7015
res4j_branch2b/kernel:0 (3, 3, 256, 256) -0.1000 +0.2422 +0.0102
bn4j_branch2b/gamma:0 (256,) +0.4069 +1.4642 +0.1343
bn4j_branch2b/beta:0 (256,) -1.9635 +0.4987 +0.3003
bn4j_branch2b/moving_mean:0 (256,) -1.0420 +0.6202 +0.2047
bn4j_branch2b/moving_variance:0 (256,) +0.0464 +0.5519 +0.0491
res4j_branch2c/kernel:0 (1, 1, 256, 1024) -0.1389 +0.1597 +0.0118
bn4j_branch2c/gamma:0 (1024,) +0.0293 +2.1061 +0.1991
bn4j_branch2c/beta:0 (1024,) -0.8361 +0.1735 +0.1254
bn4j_branch2c/moving_mean:0 (1024,) -0.2061 +0.0772 +0.0375
bn4j_branch2c/moving_variance:0 (1024,) +0.0003 +0.0274 +0.0028
res4k_branch2a/kernel:0 (1, 1, 1024, 256) -0.1359 +0.1878 +0.0112
bn4k_branch2a/gamma:0 (256,) +0.5420 +1.2074 +0.1235
bn4k_branch2a/beta:0 (256,) -1.7435 +0.3985 +0.3122
bn4k_branch2a/moving_mean:0 (256,) -6.0315 +1.7842 +1.1139
bn4k_branch2a/moving_variance:0 (256,) +0.3504 +4.6503 +0.6216
res4k_branch2b/kernel:0 (3, 3, 256, 256) -0.0792 +0.1220 +0.0093
bn4k_branch2b/gamma:0 (256,) +0.4983 +1.2338 +0.1261
bn4k_branch2b/beta:0 (256,) -1.2916 +0.1997 +0.2615
bn4k_branch2b/moving_mean:0 (256,) -1.0697 +1.5169 +0.3087
bn4k_branch2b/moving_variance:0 (256,) +0.0201 +0.4110 +0.0620
res4k_branch2c/kernel:0 (1, 1, 256, 1024) -0.1248 +0.2098 +0.0111
bn4k_branch2c/gamma:0 (1024,) +0.1164 +2.0104 +0.2095
bn4k_branch2c/beta:0 (1024,) -1.6254 +0.1871 +0.1615
bn4k_branch2c/moving_mean:0 (1024,) -0.1643 +0.0819 +0.0371
bn4k_branch2c/moving_variance:0 (1024,) +0.0018 +0.0433 +0.0044
res4l_branch2a/kernel:0 (1, 1, 1024, 256) -0.2063 +0.1837 +0.0132
bn4l_branch2a/gamma:0 (256,) +0.4153 +1.5392 +0.1226
bn4l_branch2a/beta:0 (256,) -1.8618 +0.3083 +0.3002
bn4l_branch2a/moving_mean:0 (256,) -4.3532 +1.4638 +1.0180
bn4l_branch2a/moving_variance:0 (256,) +0.6372 +5.7646 +0.7036
res4l_branch2b/kernel:0 (3, 3, 256, 256) -0.1077 +0.1703 +0.0099
bn4l_branch2b/gamma:0 (256,) +0.3988 +1.3548 +0.1309
bn4l_branch2b/beta:0 (256,) -1.4917 +0.4846 +0.2733
bn4l_branch2b/moving_mean:0 (256,) -0.4929 +0.3995 +0.1252
bn4l_branch2b/moving_variance:0 (256,) +0.0217 +0.2759 +0.0259
res4l_branch2c/kernel:0 (1, 1, 256, 1024) -0.1198 +0.1658 +0.0120
bn4l_branch2c/gamma:0 (1024,) +0.0792 +1.6846 +0.1774
bn4l_branch2c/beta:0 (1024,) -1.0420 +0.7085 +0.1541
bn4l_branch2c/moving_mean:0 (1024,) -0.1533 +0.0933 +0.0373
bn4l_branch2c/moving_variance:0 (1024,) +0.0009 +0.0339 +0.0026
res4m_branch2a/kernel:0 (1, 1, 1024, 256) -0.0796 +0.1565 +0.0116
bn4m_branch2a/gamma:0 (256,) +0.5385 +1.1934 +0.0998
bn4m_branch2a/beta:0 (256,) -1.3115 +0.3087 +0.2261
bn4m_branch2a/moving_mean:0 (256,) -5.8896 +1.3802 +1.0426
bn4m_branch2a/moving_variance:0 (256,) +0.5695 +7.6590 +0.7444
res4m_branch2b/kernel:0 (3, 3, 256, 256) -0.1025 +0.1319 +0.0090
bn4m_branch2b/gamma:0 (256,) +0.5824 +1.2019 +0.1020
bn4m_branch2b/beta:0 (256,) -1.3820 +0.1980 +0.2375
bn4m_branch2b/moving_mean:0 (256,) -0.7877 +0.6563 +0.1876
bn4m_branch2b/moving_variance:0 (256,) +0.0295 +0.3663 +0.0473
res4m_branch2c/kernel:0 (1, 1, 256, 1024) -0.1435 +0.1559 +0.0111
bn4m_branch2c/gamma:0 (1024,) +0.1877 +1.8117 +0.1711
bn4m_branch2c/beta:0 (1024,) -0.7704 +0.5870 +0.1488
bn4m_branch2c/moving_mean:0 (1024,) -0.2008 +0.0926 +0.0429
bn4m_branch2c/moving_variance:0 (1024,) +0.0013 +0.0343 +0.0039
res4n_branch2a/kernel:0 (1, 1, 1024, 256) -0.1173 +0.1577 +0.0126
bn4n_branch2a/gamma:0 (256,) +0.4684 +1.0960 +0.1128
bn4n_branch2a/beta:0 (256,) -1.2844 +0.0426 +0.2344
bn4n_branch2a/moving_mean:0 (256,) -3.2392 +1.7882 +0.8253
bn4n_branch2a/moving_variance:0 (256,) +0.6329 +3.4732 +0.4616
res4n_branch2b/kernel:0 (3, 3, 256, 256) -0.1209 +0.1524 +0.0087
bn4n_branch2b/gamma:0 (256,) +0.4896 +1.2047 +0.1105
bn4n_branch2b/beta:0 (256,) -1.0426 +0.6021 +0.2205
bn4n_branch2b/moving_mean:0 (256,) -0.3883 +0.1123 +0.0881
bn4n_branch2b/moving_variance:0 (256,) +0.0143 +0.2724 +0.0217
res4n_branch2c/kernel:0 (1, 1, 256, 1024) -0.0969 +0.1517 +0.0107
bn4n_branch2c/gamma:0 (1024,) +0.1913 +1.6900 +0.1242
bn4n_branch2c/beta:0 (1024,) -0.7635 +0.6491 +0.1329
bn4n_branch2c/moving_mean:0 (1024,) -0.2299 +0.1080 +0.0471
bn4n_branch2c/moving_variance:0 (1024,) +0.0021 +0.0429 +0.0041
res4o_branch2a/kernel:0 (1, 1, 1024, 256) -0.0868 +0.1276 +0.0122
bn4o_branch2a/gamma:0 (256,) +0.4140 +1.0878 +0.1046
bn4o_branch2a/beta:0 (256,) -1.5212 +0.1588 +0.2378
bn4o_branch2a/moving_mean:0 (256,) -6.5676 +1.6475 +1.1866
bn4o_branch2a/moving_variance:0 (256,) +0.6190 +5.9674 +0.6751
res4o_branch2b/kernel:0 (3, 3, 256, 256) -0.0956 +0.1297 +0.0088
bn4o_branch2b/gamma:0 (256,) +0.5295 +1.2251 +0.1097
bn4o_branch2b/beta:0 (256,) -1.2628 +0.4158 +0.2264
bn4o_branch2b/moving_mean:0 (256,) -0.4420 +0.3259 +0.1114
bn4o_branch2b/moving_variance:0 (256,) +0.0193 +0.1740 +0.0210
res4o_branch2c/kernel:0 (1, 1, 256, 1024) -0.1526 +0.1561 +0.0108
bn4o_branch2c/gamma:0 (1024,) +0.2372 +1.9252 +0.1541
bn4o_branch2c/beta:0 (1024,) -0.8091 +0.5670 +0.1401
bn4o_branch2c/moving_mean:0 (1024,) -0.2384 +0.1076 +0.0495
bn4o_branch2c/moving_variance:0 (1024,) +0.0016 +0.0486 +0.0047
res4p_branch2a/kernel:0 (1, 1, 1024, 256) -0.1428 +0.1984 +0.0135
bn4p_branch2a/gamma:0 (256,) +0.5017 +1.0527 +0.0912
bn4p_branch2a/beta:0 (256,) -1.4882 +0.0455 +0.2393
bn4p_branch2a/moving_mean:0 (256,) -3.1050 +2.3637 +0.9707
bn4p_branch2a/moving_variance:0 (256,) +0.7623 +3.6884 +0.5522
res4p_branch2b/kernel:0 (3, 3, 256, 256) -0.0858 +0.1100 +0.0100
bn4p_branch2b/gamma:0 (256,) +0.4397 +1.4020 +0.1318
bn4p_branch2b/beta:0 (256,) -1.4270 +0.4049 +0.2497
bn4p_branch2b/moving_mean:0 (256,) -0.4054 +0.3653 +0.1037
bn4p_branch2b/moving_variance:0 (256,) +0.0251 +0.1553 +0.0220
res4p_branch2c/kernel:0 (1, 1, 256, 1024) -0.1097 +0.1687 +0.0119
bn4p_branch2c/gamma:0 (1024,) +0.1811 +1.7263 +0.1962
bn4p_branch2c/beta:0 (1024,) -1.0450 +0.3895 +0.1635
bn4p_branch2c/moving_mean:0 (1024,) -0.2053 +0.1271 +0.0407
bn4p_branch2c/moving_variance:0 (1024,) +0.0015 +0.0479 +0.0040
res4q_branch2a/kernel:0 (1, 1, 1024, 256) -0.1232 +0.2498 +0.0136
bn4q_branch2a/gamma:0 (256,) +0.3415 +1.0128 +0.1070
bn4q_branch2a/beta:0 (256,) -1.5989 +0.3609 +0.2903
bn4q_branch2a/moving_mean:0 (256,) -5.2214 +2.3356 +1.1035
bn4q_branch2a/moving_variance:0 (256,) +0.6609 +11.6783 +0.9515
res4q_branch2b/kernel:0 (3, 3, 256, 256) -0.1798 +0.1955 +0.0088
bn4q_branch2b/gamma:0 (256,) +0.6543 +1.4769 +0.1200
bn4q_branch2b/beta:0 (256,) -1.1978 +0.3759 +0.2500
bn4q_branch2b/moving_mean:0 (256,) -0.3519 +0.1123 +0.0780
bn4q_branch2b/moving_variance:0 (256,) +0.0133 +0.1143 +0.0136
res4q_branch2c/kernel:0 (1, 1, 256, 1024) -0.1707 +0.1696 +0.0116
bn4q_branch2c/gamma:0 (1024,) +0.0371 +2.1323 +0.2144
bn4q_branch2c/beta:0 (1024,) -0.7875 +0.3563 +0.1508
bn4q_branch2c/moving_mean:0 (1024,) -0.3719 +0.2027 +0.0712
bn4q_branch2c/moving_variance:0 (1024,) +0.0013 +0.0541 +0.0063
res4r_branch2a/kernel:0 (1, 1, 1024, 256) -0.1752 +0.2489 +0.0135
bn4r_branch2a/gamma:0 (256,) +0.2799 +0.9274 +0.1076
bn4r_branch2a/beta:0 (256,) -1.3579 +0.2758 +0.2674
bn4r_branch2a/moving_mean:0 (256,) -2.7110 +3.0688 +0.9259
bn4r_branch2a/moving_variance:0 (256,) +0.8098 +9.7240 +0.7793
res4r_branch2b/kernel:0 (3, 3, 256, 256) -0.1313 +0.1872 +0.0085
bn4r_branch2b/gamma:0 (256,) +0.5145 +1.4675 +0.1384
bn4r_branch2b/beta:0 (256,) -0.9184 +0.6886 +0.2130
bn4r_branch2b/moving_mean:0 (256,) -0.2939 +0.1038 +0.0677
bn4r_branch2b/moving_variance:0 (256,) +0.0066 +0.0804 +0.0106
res4r_branch2c/kernel:0 (1, 1, 256, 1024) -0.0867 +0.1725 +0.0110
bn4r_branch2c/gamma:0 (1024,) +0.1375 +1.7613 +0.1524
bn4r_branch2c/beta:0 (1024,) -0.7547 +0.3295 +0.1355
bn4r_branch2c/moving_mean:0 (1024,) -0.2626 +0.1839 +0.0649
bn4r_branch2c/moving_variance:0 (1024,) +0.0028 +0.0670 +0.0051
res4s_branch2a/kernel:0 (1, 1, 1024, 256) -0.1282 +0.1822 +0.0127
bn4s_branch2a/gamma:0 (256,) +0.3420 +0.9798 +0.1074
bn4s_branch2a/beta:0 (256,) -1.2975 +0.5379 +0.2706
bn4s_branch2a/moving_mean:0 (256,) -8.8698 +2.0926 +1.2141
bn4s_branch2a/moving_variance:0 (256,) +0.6463 +17.2415 +1.1652
res4s_branch2b/kernel:0 (3, 3, 256, 256) -0.2004 +0.1857 +0.0084
bn4s_branch2b/gamma:0 (256,) +0.5218 +1.2066 +0.1056
bn4s_branch2b/beta:0 (256,) -1.1360 +0.3336 +0.2261
bn4s_branch2b/moving_mean:0 (256,) -0.4850 +0.1296 +0.0932
bn4s_branch2b/moving_variance:0 (256,) +0.0122 +0.0847 +0.0115
res4s_branch2c/kernel:0 (1, 1, 256, 1024) -0.1153 +0.1679 +0.0109
bn4s_branch2c/gamma:0 (1024,) +0.1504 +1.7503 +0.1436
bn4s_branch2c/beta:0 (1024,) -0.7774 +0.4413 +0.1241
bn4s_branch2c/moving_mean:0 (1024,) -0.2327 +0.1329 +0.0533
bn4s_branch2c/moving_variance:0 (1024,) +0.0022 +0.0453 +0.0037
res4t_branch2a/kernel:0 (1, 1, 1024, 256) -0.1595 +0.1765 +0.0128
bn4t_branch2a/gamma:0 (256,) +0.4376 +1.2511 +0.1069
bn4t_branch2a/beta:0 (256,) -1.1352 +0.2663 +0.2456
bn4t_branch2a/moving_mean:0 (256,) -6.3550 +2.2208 +1.3414
bn4t_branch2a/moving_variance:0 (256,) +0.7818 +5.8100 +0.6672
res4t_branch2b/kernel:0 (3, 3, 256, 256) -0.1327 +0.1067 +0.0091
bn4t_branch2b/gamma:0 (256,) +0.4616 +1.2523 +0.1079
bn4t_branch2b/beta:0 (256,) -1.1122 +0.6912 +0.2212
bn4t_branch2b/moving_mean:0 (256,) -0.8846 +0.4358 +0.1821
bn4t_branch2b/moving_variance:0 (256,) +0.0364 +0.3580 +0.0398
res4t_branch2c/kernel:0 (1, 1, 256, 1024) -0.1665 +0.1575 +0.0114
bn4t_branch2c/gamma:0 (1024,) +0.2199 +2.0075 +0.1730
bn4t_branch2c/beta:0 (1024,) -0.7963 +0.3039 +0.1346
bn4t_branch2c/moving_mean:0 (1024,) -0.2121 +0.1756 +0.0493
bn4t_branch2c/moving_variance:0 (1024,) +0.0014 +0.0391 +0.0036
res4u_branch2a/kernel:0 (1, 1, 1024, 256) -0.1065 +0.1518 +0.0119
bn4u_branch2a/gamma:0 (256,) +0.2817 +1.1194 +0.1175
bn4u_branch2a/beta:0 (256,) -1.3112 +0.5442 +0.2535
bn4u_branch2a/moving_mean:0 (256,) -9.9501 +2.8899 +1.3347
bn4u_branch2a/moving_variance:0 (256,) +0.4432 +12.1801 +1.2243
res4u_branch2b/kernel:0 (3, 3, 256, 256) -0.1258 +0.1178 +0.0078
bn4u_branch2b/gamma:0 (256,) +0.6214 +1.3642 +0.1069
bn4u_branch2b/beta:0 (256,) -0.9785 +0.4391 +0.2048
bn4u_branch2b/moving_mean:0 (256,) -0.5389 +0.4149 +0.1213
bn4u_branch2b/moving_variance:0 (256,) +0.0170 +0.1117 +0.0160
res4u_branch2c/kernel:0 (1, 1, 256, 1024) -0.1381 +0.1941 +0.0103
bn4u_branch2c/gamma:0 (1024,) +0.0992 +1.7961 +0.1407
bn4u_branch2c/beta:0 (1024,) -0.7827 +0.7017 +0.1639
bn4u_branch2c/moving_mean:0 (1024,) -0.2808 +0.1409 +0.0702
bn4u_branch2c/moving_variance:0 (1024,) +0.0023 +0.0837 +0.0066
res4v_branch2a/kernel:0 (1, 1, 1024, 256) -0.1570 +0.2220 +0.0123
bn4v_branch2a/gamma:0 (256,) +0.3942 +1.0195 +0.0944
bn4v_branch2a/beta:0 (256,) -1.2374 +0.4526 +0.2716
bn4v_branch2a/moving_mean:0 (256,) -6.7398 +2.1281 +1.2705
bn4v_branch2a/moving_variance:0 (256,) +0.6142 +6.2192 +0.7720
res4v_branch2b/kernel:0 (3, 3, 256, 256) -0.1412 +0.1655 +0.0083
bn4v_branch2b/gamma:0 (256,) +0.6196 +1.1648 +0.0919
bn4v_branch2b/beta:0 (256,) -1.0248 +0.8823 +0.1907
bn4v_branch2b/moving_mean:0 (256,) -0.5943 +0.2525 +0.1225
bn4v_branch2b/moving_variance:0 (256,) +0.0272 +0.2850 +0.0257
res4v_branch2c/kernel:0 (1, 1, 256, 1024) -0.1068 +0.1662 +0.0109
bn4v_branch2c/gamma:0 (1024,) +0.2345 +1.6791 +0.1361
bn4v_branch2c/beta:0 (1024,) -0.9361 +0.5179 +0.2008
bn4v_branch2c/moving_mean:0 (1024,) -0.3138 +0.2207 +0.0648
bn4v_branch2c/moving_variance:0 (1024,) +0.0023 +0.0550 +0.0047
res4w_branch2a/kernel:0 (1, 1, 1024, 256) -0.1353 +0.2094 +0.0127
bn4w_branch2a/gamma:0 (256,) +0.2443 +1.0933 +0.1136
bn4w_branch2a/beta:0 (256,) -1.4777 +0.3498 +0.2970
bn4w_branch2a/moving_mean:0 (256,) -15.1299 +2.8008 +2.1165
bn4w_branch2a/moving_variance:0 (256,) +0.7279 +19.8808 +1.5136
res4w_branch2b/kernel:0 (3, 3, 256, 256) -0.1029 +0.1717 +0.0083
bn4w_branch2b/gamma:0 (256,) +0.7102 +1.4165 +0.0997
bn4w_branch2b/beta:0 (256,) -0.9754 +0.3929 +0.2028
bn4w_branch2b/moving_mean:0 (256,) -0.3679 +0.2234 +0.0824
bn4w_branch2b/moving_variance:0 (256,) +0.0133 +0.1334 +0.0153
res4w_branch2c/kernel:0 (1, 1, 256, 1024) -0.1451 +0.1874 +0.0109
bn4w_branch2c/gamma:0 (1024,) +0.0215 +1.5528 +0.1530
bn4w_branch2c/beta:0 (1024,) -0.8591 +0.5082 +0.1832
bn4w_branch2c/moving_mean:0 (1024,) -0.4728 +0.1823 +0.1269
bn4w_branch2c/moving_variance:0 (1024,) +0.0008 +0.1209 +0.0090
res5a_branch2a/kernel:0 (1, 1, 1024, 512) -0.1747 +0.2130 +0.0143
bn5a_branch2a/gamma:0 (512,) +0.5045 +1.2405 +0.1245
bn5a_branch2a/beta:0 (512,) -1.4638 +0.5064 +0.3062
bn5a_branch2a/moving_mean:0 (512,) -11.4545 +4.6925 +1.6878
bn5a_branch2a/moving_variance:0 (512,) +1.1314 +15.7920 +1.4502
res5a_branch2b/kernel:0 (3, 3, 512, 512) -0.2464 +0.3249 +0.0091
bn5a_branch2b/gamma:0 (512,) +0.2982 +1.4230 +0.1373
bn5a_branch2b/beta:0 (512,) -1.6618 +0.7232 +0.3175
bn5a_branch2b/moving_mean:0 (512,) -2.2210 +1.7648 +0.3094
bn5a_branch2b/moving_variance:0 (512,) +0.1187 +1.4766 +0.1865
res5a_branch2c/kernel:0 (1, 1, 512, 2048) -0.2871 +0.3263 +0.0122
res5a_branch1/kernel:0 (1, 1, 1024, 2048) -0.3741 +0.4705 +0.0105
bn5a_branch2c/gamma:0 (2048,) +0.6692 +2.7116 +0.2395
bn5a_branch2c/beta:0 (2048,) -1.8662 +1.4781 +0.2376
bn5a_branch2c/moving_mean:0 (2048,) -0.5774 +0.6525 +0.0644
bn5a_branch2c/moving_variance:0 (2048,) +0.0024 +0.1612 +0.0084
bn5a_branch1/gamma:0 (2048,) +0.8662 +4.8957 +0.5145
bn5a_branch1/beta:0 (2048,) -1.8661 +1.4784 +0.2376
bn5a_branch1/moving_mean:0 (2048,) -10.0727 +4.4287 +1.0954
bn5a_branch1/moving_variance:0 (2048,) +0.2868 +7.7099 +0.5971
res5b_branch2a/kernel:0 (1, 1, 2048, 512) -0.1615 +0.2535 +0.0106
bn5b_branch2a/gamma:0 (512,) +0.3789 +1.1436 +0.0969
bn5b_branch2a/beta:0 (512,) -1.1929 +0.6042 +0.1990
bn5b_branch2a/moving_mean:0 (512,) -4.4332 +5.7965 +0.6776
bn5b_branch2a/moving_variance:0 (512,) +0.8363 +7.6290 +0.8946
res5b_branch2b/kernel:0 (3, 3, 512, 512) -0.1333 +0.2426 +0.0079
bn5b_branch2b/gamma:0 (512,) +0.5274 +1.1794 +0.1060
bn5b_branch2b/beta:0 (512,) -1.8549 +0.5551 +0.2810
bn5b_branch2b/moving_mean:0 (512,) -1.3069 +1.7223 +0.2265
bn5b_branch2b/moving_variance:0 (512,) +0.0572 +1.0433 +0.0724
res5b_branch2c/kernel:0 (1, 1, 512, 2048) -0.1352 +0.1977 +0.0106
bn5b_branch2c/gamma:0 (2048,) +0.5679 +2.4291 +0.2254
bn5b_branch2c/beta:0 (2048,) -2.3769 +0.1646 +0.2141
bn5b_branch2c/moving_mean:0 (2048,) -0.4428 +1.0842 +0.0525
bn5b_branch2c/moving_variance:0 (2048,) +0.0020 +0.2272 +0.0065
res5c_branch2a/kernel:0 (1, 1, 2048, 512) -0.1994 +0.3588 +0.0115
bn5c_branch2a/gamma:0 (512,) +0.1406 +1.1445 +0.0988
bn5c_branch2a/beta:0 (512,) -1.4070 +0.8456 +0.2542
bn5c_branch2a/moving_mean:0 (512,) -3.1862 +5.4617 +0.5412
bn5c_branch2a/moving_variance:0 (512,) +0.5824 +9.3422 +1.1140
res5c_branch2b/kernel:0 (3, 3, 512, 512) -0.0942 +0.0989 +0.0071
bn5c_branch2b/gamma:0 (512,) +0.4871 +1.1538 +0.0927
bn5c_branch2b/beta:0 (512,) -1.4373 +0.3356 +0.2770
bn5c_branch2b/moving_mean:0 (512,) -0.6456 +0.1676 +0.1068
bn5c_branch2b/moving_variance:0 (512,) +0.0354 +0.4077 +0.0522
res5c_branch2c/kernel:0 (1, 1, 512, 2048) -0.1317 +0.1323 +0.0103
bn5c_branch2c/gamma:0 (2048,) +0.6058 +2.5600 +0.2211
bn5c_branch2c/beta:0 (2048,) -4.0471 -0.6726 +0.2231
bn5c_branch2c/moving_mean:0 (2048,) -0.3058 +0.1791 +0.0368
bn5c_branch2c/moving_variance:0 (2048,) +0.0024 +0.0645 +0.0039
fpn_c5p5/kernel:0 (1, 1, 2048, 256) -0.0507 +0.0571 +0.0073
fpn_c5p5/bias:0 (256,) -0.0140 +0.0120 +0.0050
fpn_c4p4/kernel:0 (1, 1, 1024, 256) -0.1139 +0.0834 +0.0094
fpn_c4p4/bias:0 (256,) -0.0045 +0.0039 +0.0014
fpn_c3p3/kernel:0 (1, 1, 512, 256) -0.0509 +0.0533 +0.0073
fpn_c3p3/bias:0 (256,) -0.0061 +0.0055 +0.0020
fpn_c2p2/kernel:0 (1, 1, 256, 256) -0.0333 +0.0489 +0.0057
fpn_c2p2/bias:0 (256,) -0.0051 +0.0063 +0.0020
fpn_p5/kernel:0 (3, 3, 256, 256) -0.0338 +0.0384 +0.0054
fpn_p5/bias:0 (256,) -0.0080 +0.0079 +0.0035
fpn_p2/kernel:0 (3, 3, 256, 256) -0.0278 +0.0344 +0.0051
fpn_p2/bias:0 (256,) -0.0068 +0.0058 +0.0022
fpn_p3/kernel:0 (3, 3, 256, 256) -0.0246 +0.0288 +0.0046
fpn_p3/bias:0 (256,) -0.0039 +0.0038 +0.0015
fpn_p4/kernel:0 (3, 3, 256, 256) -0.0277 +0.0321 +0.0049
fpn_p4/bias:0 (256,) -0.0038 +0.0034 +0.0016
rpn_conv_shared/kernel:0 (3, 3, 256, 512) -0.0162 +0.0160 +0.0011
rpn_conv_shared/bias:0 (512,) -0.0012 +0.0030 +0.0004
rpn_class_raw/kernel:0 (1, 1, 512, 6) -0.0700 +0.0700 +0.0094
rpn_class_raw/bias:0 (6,) -0.0092 +0.0092 +0.0054
rpn_bbox_pred/kernel:0 (1, 1, 512, 12) -0.0844 +0.1395 +0.0139
rpn_bbox_pred/bias:0 (12,) -0.0167 +0.0201 +0.0097
mrcnn_class_conv1/kernel:0 (7, 7, 256, 1024) -0.0240 +0.0250 +0.0032
mrcnn_class_conv1/bias:0 (1024,) -0.0011 +0.0003 +0.0002
mrcnn_class_bn1/gamma:0 (1024,) +0.9650 +1.0500 +0.0079
mrcnn_class_bn1/beta:0 (1024,) -0.0320 +0.0050 +0.0033
mrcnn_class_bn1/moving_mean:0 (1024,) -20.4790 +8.7007 +2.2488
mrcnn_class_bn1/moving_variance:0 (1024,) +3.2710 +175.0298 +10.5956
mrcnn_class_conv2/kernel:0 (1, 1, 1024, 1024) -0.0632 +0.0426 +0.0051
mrcnn_class_conv2/bias:0 (1024,) -0.0156 +0.0214 +0.0039
mrcnn_class_bn2/gamma:0 (1024,) +0.9801 +1.0526 +0.0089
mrcnn_class_bn2/beta:0 (1024,) -0.0126 +0.0288 +0.0043
mrcnn_class_bn2/moving_mean:0 (1024,) -0.6272 +0.5237 +0.1323
mrcnn_class_bn2/moving_variance:0 (1024,) +0.0072 +0.6561 +0.0431
mrcnn_class_logits/kernel:0 (1024, 4) -0.0815 +0.0813 +0.0437
mrcnn_class_logits/bias:0 (4,) -0.0006 +0.0008 +0.0005
mrcnn_bbox_fc/kernel:0 (1024, 16) -0.0790 +0.0765 +0.0440
mrcnn_bbox_fc/bias:0 (16,) -0.0010 +0.0007 +0.0004
mrcnn_mask_conv1/kernel:0 (3, 3, 256, 256) -0.0520 +0.0462 +0.0045
mrcnn_mask_conv1/bias:0 (256,) -0.0037 +0.0022 +0.0009
mrcnn_mask_bn1/gamma:0 (256,) +0.9804 +1.0889 +0.0122
mrcnn_mask_bn1/beta:0 (256,) -0.0216 +0.0028 +0.0036
mrcnn_mask_bn1/moving_mean:0 (256,) -4.2402 +1.4667 +0.7274
mrcnn_mask_bn1/moving_variance:0 (256,) +0.3057 +5.8205 +0.9377
mrcnn_mask_conv2/kernel:0 (3, 3, 256, 256) -0.0544 +0.0949 +0.0045
mrcnn_mask_conv2/bias:0 (256,) -0.0049 +0.0035 +0.0016
mrcnn_mask_bn2/gamma:0 (256,) +0.9846 +1.0401 +0.0092
mrcnn_mask_bn2/beta:0 (256,) -0.0176 +0.0024 +0.0034
mrcnn_mask_bn2/moving_mean:0 (256,) -0.7085 +0.2866 +0.1437
mrcnn_mask_bn2/moving_variance:0 (256,) +0.0300 +0.3525 +0.0369
mrcnn_mask_conv3/kernel:0 (3, 3, 256, 256) -0.0416 +0.0459 +0.0042
mrcnn_mask_conv3/bias:0 (256,) -0.0107 +0.0073 +0.0028
mrcnn_mask_bn3/gamma:0 (256,) +0.9867 +1.0359 +0.0074
mrcnn_mask_bn3/beta:0 (256,) -0.0312 +0.0009 +0.0044
mrcnn_mask_bn3/moving_mean:0 (256,) -0.5781 +0.2730 +0.1416
mrcnn_mask_bn3/moving_variance:0 (256,) +0.0265 +0.1663 +0.0258
mrcnn_mask_conv4/kernel:0 (3, 3, 256, 256) -0.0326 +0.0267 +0.0037
mrcnn_mask_conv4/bias:0 (256,) -0.0014 +0.0049 +0.0009
mrcnn_mask_bn4/gamma:0 (256,) +0.9985 +1.0724 +0.0184
mrcnn_mask_bn4/beta:0 (256,) +0.0042 +0.0456 +0.0111
mrcnn_mask_bn4/moving_mean:0 (256,) -0.2408 +0.1736 +0.0773
mrcnn_mask_bn4/moving_variance:0 (256,) +0.0150 +0.0639 +0.0084
mrcnn_mask_deconv/kernel:0 (2, 2, 256, 256) -0.0273 +0.0518 +0.0047
mrcnn_mask_deconv/bias:0 (256,) -0.0029 +0.0718 +0.0104
mrcnn_mask/kernel:0 (1, 1, 256, 4) -0.1607 +0.1528 +0.0875
mrcnn_mask/bias:0 (4,) -0.0078 +0.0000 +0.0033

Histograms of Weights

TODO: cleanup this part

# Pick layer types to display
LAYER_TYPES = ['Conv2D', 'Dense', 'Conv2DTranspose']
# Get layers
layers = model.get_trainable_layers()
layers = list(filter(lambda l: l.__class__.__name__ in LAYER_TYPES, 
                layers))
# Display Histograms
fig, ax = plt.subplots(len(layers), 2, figsize=(10, 3*len(layers)),
                       gridspec_kw={"hspace":1})
for l, layer in enumerate(layers):
    weights = layer.get_weights()
    for w, weight in enumerate(weights):
        tensor = layer.weights[w]
        ax[l, w].set_title(tensor.name)
        _ = ax[l, w].hist(weight[w].flatten(), 50)

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