Biostat 203B
Display system information for reproducibility.
import IPython
print(IPython.sys_info()){'commit_hash': '8b1204b6c',
'commit_source': 'installation',
'default_encoding': 'utf-8',
'ipython_path': '/opt/venv/lib/python3.10/site-packages/IPython',
'ipython_version': '8.21.0',
'os_name': 'posix',
'platform': 'Linux-6.6.12-linuxkit-aarch64-with-glibc2.35',
'sys_executable': '/opt/venv/bin/python',
'sys_platform': 'linux',
'sys_version': '3.10.12 (main, Nov 20 2023, 15:14:05) [GCC 11.4.0]'}sessionInfo()R version 4.3.2 (2023-10-31)
Platform: aarch64-unknown-linux-gnu (64-bit)
Running under: Ubuntu 22.04.3 LTS
Matrix products: default
BLAS: /usr/lib/aarch64-linux-gnu/openblas-pthread/libblas.so.3
LAPACK: /usr/lib/aarch64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so; LAPACK version 3.10.0
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
time zone: Etc/UTC
tzcode source: system (glibc)
attached base packages:
[1] stats graphics grDevices utils datasets methods base
loaded via a namespace (and not attached):
[1] digest_0.6.34 fastmap_1.1.1 xfun_0.42 Matrix_1.6-1.1
[5] lattice_0.21-9 reticulate_1.35.0 knitr_1.45 htmltools_0.5.7
[9] png_0.1-8 rmarkdown_2.25 cli_3.6.2 grid_4.3.2
[13] compiler_4.3.2 rstudioapi_0.15.0 tools_4.3.2 evaluate_0.23
[17] Rcpp_1.0.12 yaml_2.3.8 rlang_1.1.3 jsonlite_1.8.8
[21] htmlwidgets_1.6.4Load some libraries.
# Load the pandas library
import pandas as pd
# Load numpy for array manipulation
import numpy as np
# Load seaborn plotting library
import seaborn as sns
import matplotlib.pyplot as plt
import os
# Set font sizes in plots
sns.set(font_scale = 1.2)
# Display all columns
pd.set_option('display.max_columns', None)
# Load Tensorflow and Keras
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layerslibrary(keras)
library(jpeg)
library(imager)Loading required package: magrittr
Attaching package: 'imager'The following object is masked from 'package:magrittr':
addThe following objects are masked from 'package:stats':
convolve, spectrumThe following object is masked from 'package:graphics':
frameThe following object is masked from 'package:base':
save.imageIn this example, we usea a CNN (convolution neural network) pretrained on the ImageNet database to classify natural images.
Read in images from the /book_images folder, convert them to arrays of size 224 x 224 x 3, and preprocess them using the preprocess_input function in keras.
img_dir = "book_images"
image_names = os.listdir(img_dir)
num_images = len(image_names)
x = np.empty((num_images, 224, 224, 3))
for i in range(num_images):
img_path = img_dir + '/' + image_names[i]
img = keras.utils.load_img(img_path, target_size = (224, 224))
plt.imshow(img)
x[i] = keras.utils.img_to_array(img)
plt.show(x[i] / 255)
x = keras.applications.imagenet_utils.preprocess_input(x)img_dir <- "book_images"
image_names <- list.files(img_dir)
num_images <- length(image_names)
x <- array(dim = c(num_images, 224, 224, 3))
par(mar = c(0, 0, 0, 0), mfrow = c(2, 3))
for (i in 1:num_images) {
img_path <- paste(img_dir, image_names[i], sep = "/")
plot(load.image(img_path))
img <- image_load(img_path, target_size = c(224, 224))
x[i, , ,] <- image_to_array(img)
}
x <- imagenet_preprocess_input(x)We load the resnet50 network trained on the ImageNet database.
model = keras.applications.resnet.ResNet50(weights = 'imagenet')
model.summary()Model: "resnet50"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_1 (InputLayer) [(None, 224, 224, 3)] 0 []
conv1_pad (ZeroPadding2D) (None, 230, 230, 3) 0 ['input_1[0][0]']
conv1_conv (Conv2D) (None, 112, 112, 64) 9472 ['conv1_pad[0][0]']
conv1_bn (BatchNormalizati (None, 112, 112, 64) 256 ['conv1_conv[0][0]']
on)
conv1_relu (Activation) (None, 112, 112, 64) 0 ['conv1_bn[0][0]']
pool1_pad (ZeroPadding2D) (None, 114, 114, 64) 0 ['conv1_relu[0][0]']
pool1_pool (MaxPooling2D) (None, 56, 56, 64) 0 ['pool1_pad[0][0]']
conv2_block1_1_conv (Conv2 (None, 56, 56, 64) 4160 ['pool1_pool[0][0]']
D)
conv2_block1_1_bn (BatchNo (None, 56, 56, 64) 256 ['conv2_block1_1_conv[0][0]']
rmalization)
conv2_block1_1_relu (Activ (None, 56, 56, 64) 0 ['conv2_block1_1_bn[0][0]']
ation)
conv2_block1_2_conv (Conv2 (None, 56, 56, 64) 36928 ['conv2_block1_1_relu[0][0]']
D)
conv2_block1_2_bn (BatchNo (None, 56, 56, 64) 256 ['conv2_block1_2_conv[0][0]']
rmalization)
conv2_block1_2_relu (Activ (None, 56, 56, 64) 0 ['conv2_block1_2_bn[0][0]']
ation)
conv2_block1_0_conv (Conv2 (None, 56, 56, 256) 16640 ['pool1_pool[0][0]']
D)
conv2_block1_3_conv (Conv2 (None, 56, 56, 256) 16640 ['conv2_block1_2_relu[0][0]']
D)
conv2_block1_0_bn (BatchNo (None, 56, 56, 256) 1024 ['conv2_block1_0_conv[0][0]']
rmalization)
conv2_block1_3_bn (BatchNo (None, 56, 56, 256) 1024 ['conv2_block1_3_conv[0][0]']
rmalization)
conv2_block1_add (Add) (None, 56, 56, 256) 0 ['conv2_block1_0_bn[0][0]',
'conv2_block1_3_bn[0][0]']
conv2_block1_out (Activati (None, 56, 56, 256) 0 ['conv2_block1_add[0][0]']
on)
conv2_block2_1_conv (Conv2 (None, 56, 56, 64) 16448 ['conv2_block1_out[0][0]']
D)
conv2_block2_1_bn (BatchNo (None, 56, 56, 64) 256 ['conv2_block2_1_conv[0][0]']
rmalization)
conv2_block2_1_relu (Activ (None, 56, 56, 64) 0 ['conv2_block2_1_bn[0][0]']
ation)
conv2_block2_2_conv (Conv2 (None, 56, 56, 64) 36928 ['conv2_block2_1_relu[0][0]']
D)
conv2_block2_2_bn (BatchNo (None, 56, 56, 64) 256 ['conv2_block2_2_conv[0][0]']
rmalization)
conv2_block2_2_relu (Activ (None, 56, 56, 64) 0 ['conv2_block2_2_bn[0][0]']
ation)
conv2_block2_3_conv (Conv2 (None, 56, 56, 256) 16640 ['conv2_block2_2_relu[0][0]']
D)
conv2_block2_3_bn (BatchNo (None, 56, 56, 256) 1024 ['conv2_block2_3_conv[0][0]']
rmalization)
conv2_block2_add (Add) (None, 56, 56, 256) 0 ['conv2_block1_out[0][0]',
'conv2_block2_3_bn[0][0]']
conv2_block2_out (Activati (None, 56, 56, 256) 0 ['conv2_block2_add[0][0]']
on)
conv2_block3_1_conv (Conv2 (None, 56, 56, 64) 16448 ['conv2_block2_out[0][0]']
D)
conv2_block3_1_bn (BatchNo (None, 56, 56, 64) 256 ['conv2_block3_1_conv[0][0]']
rmalization)
conv2_block3_1_relu (Activ (None, 56, 56, 64) 0 ['conv2_block3_1_bn[0][0]']
ation)
conv2_block3_2_conv (Conv2 (None, 56, 56, 64) 36928 ['conv2_block3_1_relu[0][0]']
D)
conv2_block3_2_bn (BatchNo (None, 56, 56, 64) 256 ['conv2_block3_2_conv[0][0]']
rmalization)
conv2_block3_2_relu (Activ (None, 56, 56, 64) 0 ['conv2_block3_2_bn[0][0]']
ation)
conv2_block3_3_conv (Conv2 (None, 56, 56, 256) 16640 ['conv2_block3_2_relu[0][0]']
D)
conv2_block3_3_bn (BatchNo (None, 56, 56, 256) 1024 ['conv2_block3_3_conv[0][0]']
rmalization)
conv2_block3_add (Add) (None, 56, 56, 256) 0 ['conv2_block2_out[0][0]',
'conv2_block3_3_bn[0][0]']
conv2_block3_out (Activati (None, 56, 56, 256) 0 ['conv2_block3_add[0][0]']
on)
conv3_block1_1_conv (Conv2 (None, 28, 28, 128) 32896 ['conv2_block3_out[0][0]']
D)
conv3_block1_1_bn (BatchNo (None, 28, 28, 128) 512 ['conv3_block1_1_conv[0][0]']
rmalization)
conv3_block1_1_relu (Activ (None, 28, 28, 128) 0 ['conv3_block1_1_bn[0][0]']
ation)
conv3_block1_2_conv (Conv2 (None, 28, 28, 128) 147584 ['conv3_block1_1_relu[0][0]']
D)
conv3_block1_2_bn (BatchNo (None, 28, 28, 128) 512 ['conv3_block1_2_conv[0][0]']
rmalization)
conv3_block1_2_relu (Activ (None, 28, 28, 128) 0 ['conv3_block1_2_bn[0][0]']
ation)
conv3_block1_0_conv (Conv2 (None, 28, 28, 512) 131584 ['conv2_block3_out[0][0]']
D)
conv3_block1_3_conv (Conv2 (None, 28, 28, 512) 66048 ['conv3_block1_2_relu[0][0]']
D)
conv3_block1_0_bn (BatchNo (None, 28, 28, 512) 2048 ['conv3_block1_0_conv[0][0]']
rmalization)
conv3_block1_3_bn (BatchNo (None, 28, 28, 512) 2048 ['conv3_block1_3_conv[0][0]']
rmalization)
conv3_block1_add (Add) (None, 28, 28, 512) 0 ['conv3_block1_0_bn[0][0]',
'conv3_block1_3_bn[0][0]']
conv3_block1_out (Activati (None, 28, 28, 512) 0 ['conv3_block1_add[0][0]']
on)
conv3_block2_1_conv (Conv2 (None, 28, 28, 128) 65664 ['conv3_block1_out[0][0]']
D)
conv3_block2_1_bn (BatchNo (None, 28, 28, 128) 512 ['conv3_block2_1_conv[0][0]']
rmalization)
conv3_block2_1_relu (Activ (None, 28, 28, 128) 0 ['conv3_block2_1_bn[0][0]']
ation)
conv3_block2_2_conv (Conv2 (None, 28, 28, 128) 147584 ['conv3_block2_1_relu[0][0]']
D)
conv3_block2_2_bn (BatchNo (None, 28, 28, 128) 512 ['conv3_block2_2_conv[0][0]']
rmalization)
conv3_block2_2_relu (Activ (None, 28, 28, 128) 0 ['conv3_block2_2_bn[0][0]']
ation)
conv3_block2_3_conv (Conv2 (None, 28, 28, 512) 66048 ['conv3_block2_2_relu[0][0]']
D)
conv3_block2_3_bn (BatchNo (None, 28, 28, 512) 2048 ['conv3_block2_3_conv[0][0]']
rmalization)
conv3_block2_add (Add) (None, 28, 28, 512) 0 ['conv3_block1_out[0][0]',
'conv3_block2_3_bn[0][0]']
conv3_block2_out (Activati (None, 28, 28, 512) 0 ['conv3_block2_add[0][0]']
on)
conv3_block3_1_conv (Conv2 (None, 28, 28, 128) 65664 ['conv3_block2_out[0][0]']
D)
conv3_block3_1_bn (BatchNo (None, 28, 28, 128) 512 ['conv3_block3_1_conv[0][0]']
rmalization)
conv3_block3_1_relu (Activ (None, 28, 28, 128) 0 ['conv3_block3_1_bn[0][0]']
ation)
conv3_block3_2_conv (Conv2 (None, 28, 28, 128) 147584 ['conv3_block3_1_relu[0][0]']
D)
conv3_block3_2_bn (BatchNo (None, 28, 28, 128) 512 ['conv3_block3_2_conv[0][0]']
rmalization)
conv3_block3_2_relu (Activ (None, 28, 28, 128) 0 ['conv3_block3_2_bn[0][0]']
ation)
conv3_block3_3_conv (Conv2 (None, 28, 28, 512) 66048 ['conv3_block3_2_relu[0][0]']
D)
conv3_block3_3_bn (BatchNo (None, 28, 28, 512) 2048 ['conv3_block3_3_conv[0][0]']
rmalization)
conv3_block3_add (Add) (None, 28, 28, 512) 0 ['conv3_block2_out[0][0]',
'conv3_block3_3_bn[0][0]']
conv3_block3_out (Activati (None, 28, 28, 512) 0 ['conv3_block3_add[0][0]']
on)
conv3_block4_1_conv (Conv2 (None, 28, 28, 128) 65664 ['conv3_block3_out[0][0]']
D)
conv3_block4_1_bn (BatchNo (None, 28, 28, 128) 512 ['conv3_block4_1_conv[0][0]']
rmalization)
conv3_block4_1_relu (Activ (None, 28, 28, 128) 0 ['conv3_block4_1_bn[0][0]']
ation)
conv3_block4_2_conv (Conv2 (None, 28, 28, 128) 147584 ['conv3_block4_1_relu[0][0]']
D)
conv3_block4_2_bn (BatchNo (None, 28, 28, 128) 512 ['conv3_block4_2_conv[0][0]']
rmalization)
conv3_block4_2_relu (Activ (None, 28, 28, 128) 0 ['conv3_block4_2_bn[0][0]']
ation)
conv3_block4_3_conv (Conv2 (None, 28, 28, 512) 66048 ['conv3_block4_2_relu[0][0]']
D)
conv3_block4_3_bn (BatchNo (None, 28, 28, 512) 2048 ['conv3_block4_3_conv[0][0]']
rmalization)
conv3_block4_add (Add) (None, 28, 28, 512) 0 ['conv3_block3_out[0][0]',
'conv3_block4_3_bn[0][0]']
conv3_block4_out (Activati (None, 28, 28, 512) 0 ['conv3_block4_add[0][0]']
on)
conv4_block1_1_conv (Conv2 (None, 14, 14, 256) 131328 ['conv3_block4_out[0][0]']
D)
conv4_block1_1_bn (BatchNo (None, 14, 14, 256) 1024 ['conv4_block1_1_conv[0][0]']
rmalization)
conv4_block1_1_relu (Activ (None, 14, 14, 256) 0 ['conv4_block1_1_bn[0][0]']
ation)
conv4_block1_2_conv (Conv2 (None, 14, 14, 256) 590080 ['conv4_block1_1_relu[0][0]']
D)
conv4_block1_2_bn (BatchNo (None, 14, 14, 256) 1024 ['conv4_block1_2_conv[0][0]']
rmalization)
conv4_block1_2_relu (Activ (None, 14, 14, 256) 0 ['conv4_block1_2_bn[0][0]']
ation)
conv4_block1_0_conv (Conv2 (None, 14, 14, 1024) 525312 ['conv3_block4_out[0][0]']
D)
conv4_block1_3_conv (Conv2 (None, 14, 14, 1024) 263168 ['conv4_block1_2_relu[0][0]']
D)
conv4_block1_0_bn (BatchNo (None, 14, 14, 1024) 4096 ['conv4_block1_0_conv[0][0]']
rmalization)
conv4_block1_3_bn (BatchNo (None, 14, 14, 1024) 4096 ['conv4_block1_3_conv[0][0]']
rmalization)
conv4_block1_add (Add) (None, 14, 14, 1024) 0 ['conv4_block1_0_bn[0][0]',
'conv4_block1_3_bn[0][0]']
conv4_block1_out (Activati (None, 14, 14, 1024) 0 ['conv4_block1_add[0][0]']
on)
conv4_block2_1_conv (Conv2 (None, 14, 14, 256) 262400 ['conv4_block1_out[0][0]']
D)
conv4_block2_1_bn (BatchNo (None, 14, 14, 256) 1024 ['conv4_block2_1_conv[0][0]']
rmalization)
conv4_block2_1_relu (Activ (None, 14, 14, 256) 0 ['conv4_block2_1_bn[0][0]']
ation)
conv4_block2_2_conv (Conv2 (None, 14, 14, 256) 590080 ['conv4_block2_1_relu[0][0]']
D)
conv4_block2_2_bn (BatchNo (None, 14, 14, 256) 1024 ['conv4_block2_2_conv[0][0]']
rmalization)
conv4_block2_2_relu (Activ (None, 14, 14, 256) 0 ['conv4_block2_2_bn[0][0]']
ation)
conv4_block2_3_conv (Conv2 (None, 14, 14, 1024) 263168 ['conv4_block2_2_relu[0][0]']
D)
conv4_block2_3_bn (BatchNo (None, 14, 14, 1024) 4096 ['conv4_block2_3_conv[0][0]']
rmalization)
conv4_block2_add (Add) (None, 14, 14, 1024) 0 ['conv4_block1_out[0][0]',
'conv4_block2_3_bn[0][0]']
conv4_block2_out (Activati (None, 14, 14, 1024) 0 ['conv4_block2_add[0][0]']
on)
conv4_block3_1_conv (Conv2 (None, 14, 14, 256) 262400 ['conv4_block2_out[0][0]']
D)
conv4_block3_1_bn (BatchNo (None, 14, 14, 256) 1024 ['conv4_block3_1_conv[0][0]']
rmalization)
conv4_block3_1_relu (Activ (None, 14, 14, 256) 0 ['conv4_block3_1_bn[0][0]']
ation)
conv4_block3_2_conv (Conv2 (None, 14, 14, 256) 590080 ['conv4_block3_1_relu[0][0]']
D)
conv4_block3_2_bn (BatchNo (None, 14, 14, 256) 1024 ['conv4_block3_2_conv[0][0]']
rmalization)
conv4_block3_2_relu (Activ (None, 14, 14, 256) 0 ['conv4_block3_2_bn[0][0]']
ation)
conv4_block3_3_conv (Conv2 (None, 14, 14, 1024) 263168 ['conv4_block3_2_relu[0][0]']
D)
conv4_block3_3_bn (BatchNo (None, 14, 14, 1024) 4096 ['conv4_block3_3_conv[0][0]']
rmalization)
conv4_block3_add (Add) (None, 14, 14, 1024) 0 ['conv4_block2_out[0][0]',
'conv4_block3_3_bn[0][0]']
conv4_block3_out (Activati (None, 14, 14, 1024) 0 ['conv4_block3_add[0][0]']
on)
conv4_block4_1_conv (Conv2 (None, 14, 14, 256) 262400 ['conv4_block3_out[0][0]']
D)
conv4_block4_1_bn (BatchNo (None, 14, 14, 256) 1024 ['conv4_block4_1_conv[0][0]']
rmalization)
conv4_block4_1_relu (Activ (None, 14, 14, 256) 0 ['conv4_block4_1_bn[0][0]']
ation)
conv4_block4_2_conv (Conv2 (None, 14, 14, 256) 590080 ['conv4_block4_1_relu[0][0]']
D)
conv4_block4_2_bn (BatchNo (None, 14, 14, 256) 1024 ['conv4_block4_2_conv[0][0]']
rmalization)
conv4_block4_2_relu (Activ (None, 14, 14, 256) 0 ['conv4_block4_2_bn[0][0]']
ation)
conv4_block4_3_conv (Conv2 (None, 14, 14, 1024) 263168 ['conv4_block4_2_relu[0][0]']
D)
conv4_block4_3_bn (BatchNo (None, 14, 14, 1024) 4096 ['conv4_block4_3_conv[0][0]']
rmalization)
conv4_block4_add (Add) (None, 14, 14, 1024) 0 ['conv4_block3_out[0][0]',
'conv4_block4_3_bn[0][0]']
conv4_block4_out (Activati (None, 14, 14, 1024) 0 ['conv4_block4_add[0][0]']
on)
conv4_block5_1_conv (Conv2 (None, 14, 14, 256) 262400 ['conv4_block4_out[0][0]']
D)
conv4_block5_1_bn (BatchNo (None, 14, 14, 256) 1024 ['conv4_block5_1_conv[0][0]']
rmalization)
conv4_block5_1_relu (Activ (None, 14, 14, 256) 0 ['conv4_block5_1_bn[0][0]']
ation)
conv4_block5_2_conv (Conv2 (None, 14, 14, 256) 590080 ['conv4_block5_1_relu[0][0]']
D)
conv4_block5_2_bn (BatchNo (None, 14, 14, 256) 1024 ['conv4_block5_2_conv[0][0]']
rmalization)
conv4_block5_2_relu (Activ (None, 14, 14, 256) 0 ['conv4_block5_2_bn[0][0]']
ation)
conv4_block5_3_conv (Conv2 (None, 14, 14, 1024) 263168 ['conv4_block5_2_relu[0][0]']
D)
conv4_block5_3_bn (BatchNo (None, 14, 14, 1024) 4096 ['conv4_block5_3_conv[0][0]']
rmalization)
conv4_block5_add (Add) (None, 14, 14, 1024) 0 ['conv4_block4_out[0][0]',
'conv4_block5_3_bn[0][0]']
conv4_block5_out (Activati (None, 14, 14, 1024) 0 ['conv4_block5_add[0][0]']
on)
conv4_block6_1_conv (Conv2 (None, 14, 14, 256) 262400 ['conv4_block5_out[0][0]']
D)
conv4_block6_1_bn (BatchNo (None, 14, 14, 256) 1024 ['conv4_block6_1_conv[0][0]']
rmalization)
conv4_block6_1_relu (Activ (None, 14, 14, 256) 0 ['conv4_block6_1_bn[0][0]']
ation)
conv4_block6_2_conv (Conv2 (None, 14, 14, 256) 590080 ['conv4_block6_1_relu[0][0]']
D)
conv4_block6_2_bn (BatchNo (None, 14, 14, 256) 1024 ['conv4_block6_2_conv[0][0]']
rmalization)
conv4_block6_2_relu (Activ (None, 14, 14, 256) 0 ['conv4_block6_2_bn[0][0]']
ation)
conv4_block6_3_conv (Conv2 (None, 14, 14, 1024) 263168 ['conv4_block6_2_relu[0][0]']
D)
conv4_block6_3_bn (BatchNo (None, 14, 14, 1024) 4096 ['conv4_block6_3_conv[0][0]']
rmalization)
conv4_block6_add (Add) (None, 14, 14, 1024) 0 ['conv4_block5_out[0][0]',
'conv4_block6_3_bn[0][0]']
conv4_block6_out (Activati (None, 14, 14, 1024) 0 ['conv4_block6_add[0][0]']
on)
conv5_block1_1_conv (Conv2 (None, 7, 7, 512) 524800 ['conv4_block6_out[0][0]']
D)
conv5_block1_1_bn (BatchNo (None, 7, 7, 512) 2048 ['conv5_block1_1_conv[0][0]']
rmalization)
conv5_block1_1_relu (Activ (None, 7, 7, 512) 0 ['conv5_block1_1_bn[0][0]']
ation)
conv5_block1_2_conv (Conv2 (None, 7, 7, 512) 2359808 ['conv5_block1_1_relu[0][0]']
D)
conv5_block1_2_bn (BatchNo (None, 7, 7, 512) 2048 ['conv5_block1_2_conv[0][0]']
rmalization)
conv5_block1_2_relu (Activ (None, 7, 7, 512) 0 ['conv5_block1_2_bn[0][0]']
ation)
conv5_block1_0_conv (Conv2 (None, 7, 7, 2048) 2099200 ['conv4_block6_out[0][0]']
D)
conv5_block1_3_conv (Conv2 (None, 7, 7, 2048) 1050624 ['conv5_block1_2_relu[0][0]']
D)
conv5_block1_0_bn (BatchNo (None, 7, 7, 2048) 8192 ['conv5_block1_0_conv[0][0]']
rmalization)
conv5_block1_3_bn (BatchNo (None, 7, 7, 2048) 8192 ['conv5_block1_3_conv[0][0]']
rmalization)
conv5_block1_add (Add) (None, 7, 7, 2048) 0 ['conv5_block1_0_bn[0][0]',
'conv5_block1_3_bn[0][0]']
conv5_block1_out (Activati (None, 7, 7, 2048) 0 ['conv5_block1_add[0][0]']
on)
conv5_block2_1_conv (Conv2 (None, 7, 7, 512) 1049088 ['conv5_block1_out[0][0]']
D)
conv5_block2_1_bn (BatchNo (None, 7, 7, 512) 2048 ['conv5_block2_1_conv[0][0]']
rmalization)
conv5_block2_1_relu (Activ (None, 7, 7, 512) 0 ['conv5_block2_1_bn[0][0]']
ation)
conv5_block2_2_conv (Conv2 (None, 7, 7, 512) 2359808 ['conv5_block2_1_relu[0][0]']
D)
conv5_block2_2_bn (BatchNo (None, 7, 7, 512) 2048 ['conv5_block2_2_conv[0][0]']
rmalization)
conv5_block2_2_relu (Activ (None, 7, 7, 512) 0 ['conv5_block2_2_bn[0][0]']
ation)
conv5_block2_3_conv (Conv2 (None, 7, 7, 2048) 1050624 ['conv5_block2_2_relu[0][0]']
D)
conv5_block2_3_bn (BatchNo (None, 7, 7, 2048) 8192 ['conv5_block2_3_conv[0][0]']
rmalization)
conv5_block2_add (Add) (None, 7, 7, 2048) 0 ['conv5_block1_out[0][0]',
'conv5_block2_3_bn[0][0]']
conv5_block2_out (Activati (None, 7, 7, 2048) 0 ['conv5_block2_add[0][0]']
on)
conv5_block3_1_conv (Conv2 (None, 7, 7, 512) 1049088 ['conv5_block2_out[0][0]']
D)
conv5_block3_1_bn (BatchNo (None, 7, 7, 512) 2048 ['conv5_block3_1_conv[0][0]']
rmalization)
conv5_block3_1_relu (Activ (None, 7, 7, 512) 0 ['conv5_block3_1_bn[0][0]']
ation)
conv5_block3_2_conv (Conv2 (None, 7, 7, 512) 2359808 ['conv5_block3_1_relu[0][0]']
D)
conv5_block3_2_bn (BatchNo (None, 7, 7, 512) 2048 ['conv5_block3_2_conv[0][0]']
rmalization)
conv5_block3_2_relu (Activ (None, 7, 7, 512) 0 ['conv5_block3_2_bn[0][0]']
ation)
conv5_block3_3_conv (Conv2 (None, 7, 7, 2048) 1050624 ['conv5_block3_2_relu[0][0]']
D)
conv5_block3_3_bn (BatchNo (None, 7, 7, 2048) 8192 ['conv5_block3_3_conv[0][0]']
rmalization)
conv5_block3_add (Add) (None, 7, 7, 2048) 0 ['conv5_block2_out[0][0]',
'conv5_block3_3_bn[0][0]']
conv5_block3_out (Activati (None, 7, 7, 2048) 0 ['conv5_block3_add[0][0]']
on)
avg_pool (GlobalAveragePoo (None, 2048) 0 ['conv5_block3_out[0][0]']
ling2D)
predictions (Dense) (None, 1000) 2049000 ['avg_pool[0][0]']
==================================================================================================
Total params: 25636712 (97.80 MB)
Trainable params: 25583592 (97.59 MB)
Non-trainable params: 53120 (207.50 KB)
__________________________________________________________________________________________________Plot the model:
tf.keras.utils.plot_model(
model,
to_file = "model.png",
show_shapes = True,
show_dtype = False,
show_layer_names = True,
rankdir = "TB",
expand_nested = False,
dpi = 96,
layer_range = None,
show_layer_activations = False,
)You must install pydot (`pip install pydot`) and install graphviz (see instructions at https://graphviz.gitlab.io/download/) for plot_model to work.
model <- application_resnet50(weights = "imagenet")
summary(model)Model: "resnet50"
________________________________________________________________________________
Layer (type) Output Shape Para Connected to Trainable
m #
================================================================================
input_2 (InputLay [(None, 224, 224, 0 [] Y
er) 3)]
conv1_pad (ZeroPa (None, 230, 230, 3 0 ['input_2[0][0]'] Y
dding2D) )
conv1_conv (Conv2 (None, 112, 112, 6 9472 ['conv1_pad[0][0]' Y
D) 4) ]
conv1_bn (BatchNo (None, 112, 112, 6 256 ['conv1_conv[0][0] Y
rmalization) 4) ']
conv1_relu (Activ (None, 112, 112, 6 0 ['conv1_bn[0][0]'] Y
ation) 4)
pool1_pad (ZeroPa (None, 114, 114, 6 0 ['conv1_relu[0][0] Y
dding2D) 4) ']
pool1_pool (MaxPo (None, 56, 56, 64) 0 ['pool1_pad[0][0]' Y
oling2D) ]
conv2_block1_1_co (None, 56, 56, 64) 4160 ['pool1_pool[0][0] Y
nv (Conv2D) ']
conv2_block1_1_bn (None, 56, 56, 64) 256 ['conv2_block1_1_c Y
(BatchNormalizat onv[0][0]']
ion)
conv2_block1_1_re (None, 56, 56, 64) 0 ['conv2_block1_1_b Y
lu (Activation) n[0][0]']
conv2_block1_2_co (None, 56, 56, 64) 3692 ['conv2_block1_1_r Y
nv (Conv2D) 8 elu[0][0]']
conv2_block1_2_bn (None, 56, 56, 64) 256 ['conv2_block1_2_c Y
(BatchNormalizat onv[0][0]']
ion)
conv2_block1_2_re (None, 56, 56, 64) 0 ['conv2_block1_2_b Y
lu (Activation) n[0][0]']
conv2_block1_0_co (None, 56, 56, 256 1664 ['pool1_pool[0][0] Y
nv (Conv2D) ) 0 ']
conv2_block1_3_co (None, 56, 56, 256 1664 ['conv2_block1_2_r Y
nv (Conv2D) ) 0 elu[0][0]']
conv2_block1_0_bn (None, 56, 56, 256 1024 ['conv2_block1_0_c Y
(BatchNormalizat ) onv[0][0]']
ion)
conv2_block1_3_bn (None, 56, 56, 256 1024 ['conv2_block1_3_c Y
(BatchNormalizat ) onv[0][0]']
ion)
conv2_block1_add (None, 56, 56, 256 0 ['conv2_block1_0_b Y
(Add) ) n[0][0]',
'conv2_block1_3_b
n[0][0]']
conv2_block1_out (None, 56, 56, 256 0 ['conv2_block1_add Y
(Activation) ) [0][0]']
conv2_block2_1_co (None, 56, 56, 64) 1644 ['conv2_block1_out Y
nv (Conv2D) 8 [0][0]']
conv2_block2_1_bn (None, 56, 56, 64) 256 ['conv2_block2_1_c Y
(BatchNormalizat onv[0][0]']
ion)
conv2_block2_1_re (None, 56, 56, 64) 0 ['conv2_block2_1_b Y
lu (Activation) n[0][0]']
conv2_block2_2_co (None, 56, 56, 64) 3692 ['conv2_block2_1_r Y
nv (Conv2D) 8 elu[0][0]']
conv2_block2_2_bn (None, 56, 56, 64) 256 ['conv2_block2_2_c Y
(BatchNormalizat onv[0][0]']
ion)
conv2_block2_2_re (None, 56, 56, 64) 0 ['conv2_block2_2_b Y
lu (Activation) n[0][0]']
conv2_block2_3_co (None, 56, 56, 256 1664 ['conv2_block2_2_r Y
nv (Conv2D) ) 0 elu[0][0]']
conv2_block2_3_bn (None, 56, 56, 256 1024 ['conv2_block2_3_c Y
(BatchNormalizat ) onv[0][0]']
ion)
conv2_block2_add (None, 56, 56, 256 0 ['conv2_block1_out Y
(Add) ) [0][0]',
'conv2_block2_3_b
n[0][0]']
conv2_block2_out (None, 56, 56, 256 0 ['conv2_block2_add Y
(Activation) ) [0][0]']
conv2_block3_1_co (None, 56, 56, 64) 1644 ['conv2_block2_out Y
nv (Conv2D) 8 [0][0]']
conv2_block3_1_bn (None, 56, 56, 64) 256 ['conv2_block3_1_c Y
(BatchNormalizat onv[0][0]']
ion)
conv2_block3_1_re (None, 56, 56, 64) 0 ['conv2_block3_1_b Y
lu (Activation) n[0][0]']
conv2_block3_2_co (None, 56, 56, 64) 3692 ['conv2_block3_1_r Y
nv (Conv2D) 8 elu[0][0]']
conv2_block3_2_bn (None, 56, 56, 64) 256 ['conv2_block3_2_c Y
(BatchNormalizat onv[0][0]']
ion)
conv2_block3_2_re (None, 56, 56, 64) 0 ['conv2_block3_2_b Y
lu (Activation) n[0][0]']
conv2_block3_3_co (None, 56, 56, 256 1664 ['conv2_block3_2_r Y
nv (Conv2D) ) 0 elu[0][0]']
conv2_block3_3_bn (None, 56, 56, 256 1024 ['conv2_block3_3_c Y
(BatchNormalizat ) onv[0][0]']
ion)
conv2_block3_add (None, 56, 56, 256 0 ['conv2_block2_out Y
(Add) ) [0][0]',
'conv2_block3_3_b
n[0][0]']
conv2_block3_out (None, 56, 56, 256 0 ['conv2_block3_add Y
(Activation) ) [0][0]']
conv3_block1_1_co (None, 28, 28, 128 3289 ['conv2_block3_out Y
nv (Conv2D) ) 6 [0][0]']
conv3_block1_1_bn (None, 28, 28, 128 512 ['conv3_block1_1_c Y
(BatchNormalizat ) onv[0][0]']
ion)
conv3_block1_1_re (None, 28, 28, 128 0 ['conv3_block1_1_b Y
lu (Activation) ) n[0][0]']
conv3_block1_2_co (None, 28, 28, 128 1475 ['conv3_block1_1_r Y
nv (Conv2D) ) 84 elu[0][0]']
conv3_block1_2_bn (None, 28, 28, 128 512 ['conv3_block1_2_c Y
(BatchNormalizat ) onv[0][0]']
ion)
conv3_block1_2_re (None, 28, 28, 128 0 ['conv3_block1_2_b Y
lu (Activation) ) n[0][0]']
conv3_block1_0_co (None, 28, 28, 512 1315 ['conv2_block3_out Y
nv (Conv2D) ) 84 [0][0]']
conv3_block1_3_co (None, 28, 28, 512 6604 ['conv3_block1_2_r Y
nv (Conv2D) ) 8 elu[0][0]']
conv3_block1_0_bn (None, 28, 28, 512 2048 ['conv3_block1_0_c Y
(BatchNormalizat ) onv[0][0]']
ion)
conv3_block1_3_bn (None, 28, 28, 512 2048 ['conv3_block1_3_c Y
(BatchNormalizat ) onv[0][0]']
ion)
conv3_block1_add (None, 28, 28, 512 0 ['conv3_block1_0_b Y
(Add) ) n[0][0]',
'conv3_block1_3_b
n[0][0]']
conv3_block1_out (None, 28, 28, 512 0 ['conv3_block1_add Y
(Activation) ) [0][0]']
conv3_block2_1_co (None, 28, 28, 128 6566 ['conv3_block1_out Y
nv (Conv2D) ) 4 [0][0]']
conv3_block2_1_bn (None, 28, 28, 128 512 ['conv3_block2_1_c Y
(BatchNormalizat ) onv[0][0]']
ion)
conv3_block2_1_re (None, 28, 28, 128 0 ['conv3_block2_1_b Y
lu (Activation) ) n[0][0]']
conv3_block2_2_co (None, 28, 28, 128 1475 ['conv3_block2_1_r Y
nv (Conv2D) ) 84 elu[0][0]']
conv3_block2_2_bn (None, 28, 28, 128 512 ['conv3_block2_2_c Y
(BatchNormalizat ) onv[0][0]']
ion)
conv3_block2_2_re (None, 28, 28, 128 0 ['conv3_block2_2_b Y
lu (Activation) ) n[0][0]']
conv3_block2_3_co (None, 28, 28, 512 6604 ['conv3_block2_2_r Y
nv (Conv2D) ) 8 elu[0][0]']
conv3_block2_3_bn (None, 28, 28, 512 2048 ['conv3_block2_3_c Y
(BatchNormalizat ) onv[0][0]']
ion)
conv3_block2_add (None, 28, 28, 512 0 ['conv3_block1_out Y
(Add) ) [0][0]',
'conv3_block2_3_b
n[0][0]']
conv3_block2_out (None, 28, 28, 512 0 ['conv3_block2_add Y
(Activation) ) [0][0]']
conv3_block3_1_co (None, 28, 28, 128 6566 ['conv3_block2_out Y
nv (Conv2D) ) 4 [0][0]']
conv3_block3_1_bn (None, 28, 28, 128 512 ['conv3_block3_1_c Y
(BatchNormalizat ) onv[0][0]']
ion)
conv3_block3_1_re (None, 28, 28, 128 0 ['conv3_block3_1_b Y
lu (Activation) ) n[0][0]']
conv3_block3_2_co (None, 28, 28, 128 1475 ['conv3_block3_1_r Y
nv (Conv2D) ) 84 elu[0][0]']
conv3_block3_2_bn (None, 28, 28, 128 512 ['conv3_block3_2_c Y
(BatchNormalizat ) onv[0][0]']
ion)
conv3_block3_2_re (None, 28, 28, 128 0 ['conv3_block3_2_b Y
lu (Activation) ) n[0][0]']
conv3_block3_3_co (None, 28, 28, 512 6604 ['conv3_block3_2_r Y
nv (Conv2D) ) 8 elu[0][0]']
conv3_block3_3_bn (None, 28, 28, 512 2048 ['conv3_block3_3_c Y
(BatchNormalizat ) onv[0][0]']
ion)
conv3_block3_add (None, 28, 28, 512 0 ['conv3_block2_out Y
(Add) ) [0][0]',
'conv3_block3_3_b
n[0][0]']
conv3_block3_out (None, 28, 28, 512 0 ['conv3_block3_add Y
(Activation) ) [0][0]']
conv3_block4_1_co (None, 28, 28, 128 6566 ['conv3_block3_out Y
nv (Conv2D) ) 4 [0][0]']
conv3_block4_1_bn (None, 28, 28, 128 512 ['conv3_block4_1_c Y
(BatchNormalizat ) onv[0][0]']
ion)
conv3_block4_1_re (None, 28, 28, 128 0 ['conv3_block4_1_b Y
lu (Activation) ) n[0][0]']
conv3_block4_2_co (None, 28, 28, 128 1475 ['conv3_block4_1_r Y
nv (Conv2D) ) 84 elu[0][0]']
conv3_block4_2_bn (None, 28, 28, 128 512 ['conv3_block4_2_c Y
(BatchNormalizat ) onv[0][0]']
ion)
conv3_block4_2_re (None, 28, 28, 128 0 ['conv3_block4_2_b Y
lu (Activation) ) n[0][0]']
conv3_block4_3_co (None, 28, 28, 512 6604 ['conv3_block4_2_r Y
nv (Conv2D) ) 8 elu[0][0]']
conv3_block4_3_bn (None, 28, 28, 512 2048 ['conv3_block4_3_c Y
(BatchNormalizat ) onv[0][0]']
ion)
conv3_block4_add (None, 28, 28, 512 0 ['conv3_block3_out Y
(Add) ) [0][0]',
'conv3_block4_3_b
n[0][0]']
conv3_block4_out (None, 28, 28, 512 0 ['conv3_block4_add Y
(Activation) ) [0][0]']
conv4_block1_1_co (None, 14, 14, 256 1313 ['conv3_block4_out Y
nv (Conv2D) ) 28 [0][0]']
conv4_block1_1_bn (None, 14, 14, 256 1024 ['conv4_block1_1_c Y
(BatchNormalizat ) onv[0][0]']
ion)
conv4_block1_1_re (None, 14, 14, 256 0 ['conv4_block1_1_b Y
lu (Activation) ) n[0][0]']
conv4_block1_2_co (None, 14, 14, 256 5900 ['conv4_block1_1_r Y
nv (Conv2D) ) 80 elu[0][0]']
conv4_block1_2_bn (None, 14, 14, 256 1024 ['conv4_block1_2_c Y
(BatchNormalizat ) onv[0][0]']
ion)
conv4_block1_2_re (None, 14, 14, 256 0 ['conv4_block1_2_b Y
lu (Activation) ) n[0][0]']
conv4_block1_0_co (None, 14, 14, 102 5253 ['conv3_block4_out Y
nv (Conv2D) 4) 12 [0][0]']
conv4_block1_3_co (None, 14, 14, 102 2631 ['conv4_block1_2_r Y
nv (Conv2D) 4) 68 elu[0][0]']
conv4_block1_0_bn (None, 14, 14, 102 4096 ['conv4_block1_0_c Y
(BatchNormalizat 4) onv[0][0]']
ion)
conv4_block1_3_bn (None, 14, 14, 102 4096 ['conv4_block1_3_c Y
(BatchNormalizat 4) onv[0][0]']
ion)
conv4_block1_add (None, 14, 14, 102 0 ['conv4_block1_0_b Y
(Add) 4) n[0][0]',
'conv4_block1_3_b
n[0][0]']
conv4_block1_out (None, 14, 14, 102 0 ['conv4_block1_add Y
(Activation) 4) [0][0]']
conv4_block2_1_co (None, 14, 14, 256 2624 ['conv4_block1_out Y
nv (Conv2D) ) 00 [0][0]']
conv4_block2_1_bn (None, 14, 14, 256 1024 ['conv4_block2_1_c Y
(BatchNormalizat ) onv[0][0]']
ion)
conv4_block2_1_re (None, 14, 14, 256 0 ['conv4_block2_1_b Y
lu (Activation) ) n[0][0]']
conv4_block2_2_co (None, 14, 14, 256 5900 ['conv4_block2_1_r Y
nv (Conv2D) ) 80 elu[0][0]']
conv4_block2_2_bn (None, 14, 14, 256 1024 ['conv4_block2_2_c Y
(BatchNormalizat ) onv[0][0]']
ion)
conv4_block2_2_re (None, 14, 14, 256 0 ['conv4_block2_2_b Y
lu (Activation) ) n[0][0]']
conv4_block2_3_co (None, 14, 14, 102 2631 ['conv4_block2_2_r Y
nv (Conv2D) 4) 68 elu[0][0]']
conv4_block2_3_bn (None, 14, 14, 102 4096 ['conv4_block2_3_c Y
(BatchNormalizat 4) onv[0][0]']
ion)
conv4_block2_add (None, 14, 14, 102 0 ['conv4_block1_out Y
(Add) 4) [0][0]',
'conv4_block2_3_b
n[0][0]']
conv4_block2_out (None, 14, 14, 102 0 ['conv4_block2_add Y
(Activation) 4) [0][0]']
conv4_block3_1_co (None, 14, 14, 256 2624 ['conv4_block2_out Y
nv (Conv2D) ) 00 [0][0]']
conv4_block3_1_bn (None, 14, 14, 256 1024 ['conv4_block3_1_c Y
(BatchNormalizat ) onv[0][0]']
ion)
conv4_block3_1_re (None, 14, 14, 256 0 ['conv4_block3_1_b Y
lu (Activation) ) n[0][0]']
conv4_block3_2_co (None, 14, 14, 256 5900 ['conv4_block3_1_r Y
nv (Conv2D) ) 80 elu[0][0]']
conv4_block3_2_bn (None, 14, 14, 256 1024 ['conv4_block3_2_c Y
(BatchNormalizat ) onv[0][0]']
ion)
conv4_block3_2_re (None, 14, 14, 256 0 ['conv4_block3_2_b Y
lu (Activation) ) n[0][0]']
conv4_block3_3_co (None, 14, 14, 102 2631 ['conv4_block3_2_r Y
nv (Conv2D) 4) 68 elu[0][0]']
conv4_block3_3_bn (None, 14, 14, 102 4096 ['conv4_block3_3_c Y
(BatchNormalizat 4) onv[0][0]']
ion)
conv4_block3_add (None, 14, 14, 102 0 ['conv4_block2_out Y
(Add) 4) [0][0]',
'conv4_block3_3_b
n[0][0]']
conv4_block3_out (None, 14, 14, 102 0 ['conv4_block3_add Y
(Activation) 4) [0][0]']
conv4_block4_1_co (None, 14, 14, 256 2624 ['conv4_block3_out Y
nv (Conv2D) ) 00 [0][0]']
conv4_block4_1_bn (None, 14, 14, 256 1024 ['conv4_block4_1_c Y
(BatchNormalizat ) onv[0][0]']
ion)
conv4_block4_1_re (None, 14, 14, 256 0 ['conv4_block4_1_b Y
lu (Activation) ) n[0][0]']
conv4_block4_2_co (None, 14, 14, 256 5900 ['conv4_block4_1_r Y
nv (Conv2D) ) 80 elu[0][0]']
conv4_block4_2_bn (None, 14, 14, 256 1024 ['conv4_block4_2_c Y
(BatchNormalizat ) onv[0][0]']
ion)
conv4_block4_2_re (None, 14, 14, 256 0 ['conv4_block4_2_b Y
lu (Activation) ) n[0][0]']
conv4_block4_3_co (None, 14, 14, 102 2631 ['conv4_block4_2_r Y
nv (Conv2D) 4) 68 elu[0][0]']
conv4_block4_3_bn (None, 14, 14, 102 4096 ['conv4_block4_3_c Y
(BatchNormalizat 4) onv[0][0]']
ion)
conv4_block4_add (None, 14, 14, 102 0 ['conv4_block3_out Y
(Add) 4) [0][0]',
'conv4_block4_3_b
n[0][0]']
conv4_block4_out (None, 14, 14, 102 0 ['conv4_block4_add Y
(Activation) 4) [0][0]']
conv4_block5_1_co (None, 14, 14, 256 2624 ['conv4_block4_out Y
nv (Conv2D) ) 00 [0][0]']
conv4_block5_1_bn (None, 14, 14, 256 1024 ['conv4_block5_1_c Y
(BatchNormalizat ) onv[0][0]']
ion)
conv4_block5_1_re (None, 14, 14, 256 0 ['conv4_block5_1_b Y
lu (Activation) ) n[0][0]']
conv4_block5_2_co (None, 14, 14, 256 5900 ['conv4_block5_1_r Y
nv (Conv2D) ) 80 elu[0][0]']
conv4_block5_2_bn (None, 14, 14, 256 1024 ['conv4_block5_2_c Y
(BatchNormalizat ) onv[0][0]']
ion)
conv4_block5_2_re (None, 14, 14, 256 0 ['conv4_block5_2_b Y
lu (Activation) ) n[0][0]']
conv4_block5_3_co (None, 14, 14, 102 2631 ['conv4_block5_2_r Y
nv (Conv2D) 4) 68 elu[0][0]']
conv4_block5_3_bn (None, 14, 14, 102 4096 ['conv4_block5_3_c Y
(BatchNormalizat 4) onv[0][0]']
ion)
conv4_block5_add (None, 14, 14, 102 0 ['conv4_block4_out Y
(Add) 4) [0][0]',
'conv4_block5_3_b
n[0][0]']
conv4_block5_out (None, 14, 14, 102 0 ['conv4_block5_add Y
(Activation) 4) [0][0]']
conv4_block6_1_co (None, 14, 14, 256 2624 ['conv4_block5_out Y
nv (Conv2D) ) 00 [0][0]']
conv4_block6_1_bn (None, 14, 14, 256 1024 ['conv4_block6_1_c Y
(BatchNormalizat ) onv[0][0]']
ion)
conv4_block6_1_re (None, 14, 14, 256 0 ['conv4_block6_1_b Y
lu (Activation) ) n[0][0]']
conv4_block6_2_co (None, 14, 14, 256 5900 ['conv4_block6_1_r Y
nv (Conv2D) ) 80 elu[0][0]']
conv4_block6_2_bn (None, 14, 14, 256 1024 ['conv4_block6_2_c Y
(BatchNormalizat ) onv[0][0]']
ion)
conv4_block6_2_re (None, 14, 14, 256 0 ['conv4_block6_2_b Y
lu (Activation) ) n[0][0]']
conv4_block6_3_co (None, 14, 14, 102 2631 ['conv4_block6_2_r Y
nv (Conv2D) 4) 68 elu[0][0]']
conv4_block6_3_bn (None, 14, 14, 102 4096 ['conv4_block6_3_c Y
(BatchNormalizat 4) onv[0][0]']
ion)
conv4_block6_add (None, 14, 14, 102 0 ['conv4_block5_out Y
(Add) 4) [0][0]',
'conv4_block6_3_b
n[0][0]']
conv4_block6_out (None, 14, 14, 102 0 ['conv4_block6_add Y
(Activation) 4) [0][0]']
conv5_block1_1_co (None, 7, 7, 512) 5248 ['conv4_block6_out Y
nv (Conv2D) 00 [0][0]']
conv5_block1_1_bn (None, 7, 7, 512) 2048 ['conv5_block1_1_c Y
(BatchNormalizat onv[0][0]']
ion)
conv5_block1_1_re (None, 7, 7, 512) 0 ['conv5_block1_1_b Y
lu (Activation) n[0][0]']
conv5_block1_2_co (None, 7, 7, 512) 2359 ['conv5_block1_1_r Y
nv (Conv2D) 808 elu[0][0]']
conv5_block1_2_bn (None, 7, 7, 512) 2048 ['conv5_block1_2_c Y
(BatchNormalizat onv[0][0]']
ion)
conv5_block1_2_re (None, 7, 7, 512) 0 ['conv5_block1_2_b Y
lu (Activation) n[0][0]']
conv5_block1_0_co (None, 7, 7, 2048) 2099 ['conv4_block6_out Y
nv (Conv2D) 200 [0][0]']
conv5_block1_3_co (None, 7, 7, 2048) 1050 ['conv5_block1_2_r Y
nv (Conv2D) 624 elu[0][0]']
conv5_block1_0_bn (None, 7, 7, 2048) 8192 ['conv5_block1_0_c Y
(BatchNormalizat onv[0][0]']
ion)
conv5_block1_3_bn (None, 7, 7, 2048) 8192 ['conv5_block1_3_c Y
(BatchNormalizat onv[0][0]']
ion)
conv5_block1_add (None, 7, 7, 2048) 0 ['conv5_block1_0_b Y
(Add) n[0][0]',
'conv5_block1_3_b
n[0][0]']
conv5_block1_out (None, 7, 7, 2048) 0 ['conv5_block1_add Y
(Activation) [0][0]']
conv5_block2_1_co (None, 7, 7, 512) 1049 ['conv5_block1_out Y
nv (Conv2D) 088 [0][0]']
conv5_block2_1_bn (None, 7, 7, 512) 2048 ['conv5_block2_1_c Y
(BatchNormalizat onv[0][0]']
ion)
conv5_block2_1_re (None, 7, 7, 512) 0 ['conv5_block2_1_b Y
lu (Activation) n[0][0]']
conv5_block2_2_co (None, 7, 7, 512) 2359 ['conv5_block2_1_r Y
nv (Conv2D) 808 elu[0][0]']
conv5_block2_2_bn (None, 7, 7, 512) 2048 ['conv5_block2_2_c Y
(BatchNormalizat onv[0][0]']
ion)
conv5_block2_2_re (None, 7, 7, 512) 0 ['conv5_block2_2_b Y
lu (Activation) n[0][0]']
conv5_block2_3_co (None, 7, 7, 2048) 1050 ['conv5_block2_2_r Y
nv (Conv2D) 624 elu[0][0]']
conv5_block2_3_bn (None, 7, 7, 2048) 8192 ['conv5_block2_3_c Y
(BatchNormalizat onv[0][0]']
ion)
conv5_block2_add (None, 7, 7, 2048) 0 ['conv5_block1_out Y
(Add) [0][0]',
'conv5_block2_3_b
n[0][0]']
conv5_block2_out (None, 7, 7, 2048) 0 ['conv5_block2_add Y
(Activation) [0][0]']
conv5_block3_1_co (None, 7, 7, 512) 1049 ['conv5_block2_out Y
nv (Conv2D) 088 [0][0]']
conv5_block3_1_bn (None, 7, 7, 512) 2048 ['conv5_block3_1_c Y
(BatchNormalizat onv[0][0]']
ion)
conv5_block3_1_re (None, 7, 7, 512) 0 ['conv5_block3_1_b Y
lu (Activation) n[0][0]']
conv5_block3_2_co (None, 7, 7, 512) 2359 ['conv5_block3_1_r Y
nv (Conv2D) 808 elu[0][0]']
conv5_block3_2_bn (None, 7, 7, 512) 2048 ['conv5_block3_2_c Y
(BatchNormalizat onv[0][0]']
ion)
conv5_block3_2_re (None, 7, 7, 512) 0 ['conv5_block3_2_b Y
lu (Activation) n[0][0]']
conv5_block3_3_co (None, 7, 7, 2048) 1050 ['conv5_block3_2_r Y
nv (Conv2D) 624 elu[0][0]']
conv5_block3_3_bn (None, 7, 7, 2048) 8192 ['conv5_block3_3_c Y
(BatchNormalizat onv[0][0]']
ion)
conv5_block3_add (None, 7, 7, 2048) 0 ['conv5_block2_out Y
(Add) [0][0]',
'conv5_block3_3_b
n[0][0]']
conv5_block3_out (None, 7, 7, 2048) 0 ['conv5_block3_add Y
(Activation) [0][0]']
avg_pool (GlobalA (None, 2048) 0 ['conv5_block3_out Y
veragePooling2D) [0][0]']
predictions (Dens (None, 1000) 2049 ['avg_pool[0][0]'] Y
e) 000
================================================================================
Total params: 25636712 (97.80 MB)
Trainable params: 25583592 (97.59 MB)
Non-trainable params: 53120 (207.50 KB)
________________________________________________________________________________Finally, we classify our six images, and return the top three class choices in terms of predicted probability for each.
pred6 = keras.applications.imagenet_utils.decode_predictions(
model.predict(x),
top = 3
)
1/1 [==============================] - ETA: 0s
1/1 [==============================] - 0s 415ms/stepfor i in range(num_images):
print(image_names[i])
print(np.asarray(pred6[i]))weaver.jpg
[['n01843065' 'jacamar' '0.49795353']
['n01818515' 'macaw' '0.22193344']
['n02494079' 'squirrel_monkey' '0.04287851']]
flamingo.jpg
[['n02007558' 'flamingo' '0.92635']
['n02006656' 'spoonbill' '0.071699165']
['n02002556' 'white_stork' '0.0012282135']]
huey.jpg
[['n02097474' 'Tibetan_terrier' '0.509297']
['n02098413' 'Lhasa' '0.42209885']
['n02098105' 'soft-coated_wheaten_terrier' '0.016958572']]
hawk_cropped.jpeg
[['n01608432' 'kite' '0.7227091']
['n01622779' 'great_grey_owl' '0.08182563']
['n01532829' 'house_finch' '0.04218885']]
hawk.jpg
[['n03388043' 'fountain' '0.27886552']
['n03532672' 'hook' '0.17855462']
['n03804744' 'nail' '0.10807269']]
kitty.jpg
[['n02105641' 'Old_English_sheepdog' '0.8326595']
['n02086240' 'Shih-Tzu' '0.04513897']
['n03223299' 'doormat' '0.0329979']]pred6 <- model %>%
predict(x) %>%
imagenet_decode_predictions(top = 3)1/1 - 0s - 395ms/epoch - 395ms/stepnames(pred6) <- image_names
pred6$flamingo.jpg
class_name class_description score
1 n02007558 flamingo 0.926349998
2 n02006656 spoonbill 0.071699165
3 n02002556 white_stork 0.001228208
$hawk_cropped.jpeg
class_name class_description score
1 n01608432 kite 0.72270912
2 n01622779 great_grey_owl 0.08182571
3 n01532829 house_finch 0.04218881
$hawk.jpg
class_name class_description score
1 n03388043 fountain 0.2788656
2 n03532672 hook 0.1785545
3 n03804744 nail 0.1080726
$huey.jpg
class_name class_description score
1 n02097474 Tibetan_terrier 0.50929749
2 n02098413 Lhasa 0.42209846
3 n02098105 soft-coated_wheaten_terrier 0.01695856
$kitty.jpg
class_name class_description score
1 n02105641 Old_English_sheepdog 0.83265990
2 n02086240 Shih-Tzu 0.04513899
3 n03223299 doormat 0.03299772
$weaver.jpg
class_name class_description score
1 n01843065 jacamar 0.49795401
2 n01818515 macaw 0.22193280
3 n02494079 squirrel_monkey 0.04287859