Econ 425T / Biostat 203B
Display system information for reproducibility.
import IPython
print(IPython.sys_info()){'commit_hash': 'add5877a4',
'commit_source': 'installation',
'default_encoding': 'utf-8',
'ipython_path': '/Users/huazhou/opt/anaconda3/lib/python3.9/site-packages/IPython',
'ipython_version': '8.8.0',
'os_name': 'posix',
'platform': 'macOS-10.16-x86_64-i386-64bit',
'sys_executable': '/Users/huazhou/opt/anaconda3/bin/python3',
'sys_platform': 'darwin',
'sys_version': '3.9.12 (main, Apr 5 2022, 01:56:13) \n[Clang 12.0.0 ]'}sessionInfo()R version 4.2.2 (2022-10-31)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Big Sur ... 10.16
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
loaded via a namespace (and not attached):
[1] Rcpp_1.0.9 here_1.0.1 lattice_0.20-45 png_0.1-8
[5] withr_2.5.0 rprojroot_2.0.3 digest_0.6.29 grid_4.2.2
[9] jsonlite_1.8.0 magrittr_2.0.3 evaluate_0.15 rlang_1.0.4
[13] stringi_1.7.8 cli_3.4.1 rstudioapi_0.13 Matrix_1.5-1
[17] reticulate_1.27 rmarkdown_2.14 tools_4.2.2 stringr_1.4.0
[21] htmlwidgets_1.6.1 xfun_0.31 yaml_2.3.5 fastmap_1.1.0
[25] compiler_4.2.2 htmltools_0.5.4 knitr_1.39 Load 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)In 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 (BatchNormalization) (None, 112, 112, 64 256 ['conv1_conv[0][0]']
)
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 (Conv2D) (None, 56, 56, 64) 4160 ['pool1_pool[0][0]']
conv2_block1_1_bn (BatchNormal (None, 56, 56, 64) 256 ['conv2_block1_1_conv[0][0]']
ization)
conv2_block1_1_relu (Activatio (None, 56, 56, 64) 0 ['conv2_block1_1_bn[0][0]']
n)
conv2_block1_2_conv (Conv2D) (None, 56, 56, 64) 36928 ['conv2_block1_1_relu[0][0]']
conv2_block1_2_bn (BatchNormal (None, 56, 56, 64) 256 ['conv2_block1_2_conv[0][0]']
ization)
conv2_block1_2_relu (Activatio (None, 56, 56, 64) 0 ['conv2_block1_2_bn[0][0]']
n)
conv2_block1_0_conv (Conv2D) (None, 56, 56, 256) 16640 ['pool1_pool[0][0]']
conv2_block1_3_conv (Conv2D) (None, 56, 56, 256) 16640 ['conv2_block1_2_relu[0][0]']
conv2_block1_0_bn (BatchNormal (None, 56, 56, 256) 1024 ['conv2_block1_0_conv[0][0]']
ization)
conv2_block1_3_bn (BatchNormal (None, 56, 56, 256) 1024 ['conv2_block1_3_conv[0][0]']
ization)
conv2_block1_add (Add) (None, 56, 56, 256) 0 ['conv2_block1_0_bn[0][0]',
'conv2_block1_3_bn[0][0]']
conv2_block1_out (Activation) (None, 56, 56, 256) 0 ['conv2_block1_add[0][0]']
conv2_block2_1_conv (Conv2D) (None, 56, 56, 64) 16448 ['conv2_block1_out[0][0]']
conv2_block2_1_bn (BatchNormal (None, 56, 56, 64) 256 ['conv2_block2_1_conv[0][0]']
ization)
conv2_block2_1_relu (Activatio (None, 56, 56, 64) 0 ['conv2_block2_1_bn[0][0]']
n)
conv2_block2_2_conv (Conv2D) (None, 56, 56, 64) 36928 ['conv2_block2_1_relu[0][0]']
conv2_block2_2_bn (BatchNormal (None, 56, 56, 64) 256 ['conv2_block2_2_conv[0][0]']
ization)
conv2_block2_2_relu (Activatio (None, 56, 56, 64) 0 ['conv2_block2_2_bn[0][0]']
n)
conv2_block2_3_conv (Conv2D) (None, 56, 56, 256) 16640 ['conv2_block2_2_relu[0][0]']
conv2_block2_3_bn (BatchNormal (None, 56, 56, 256) 1024 ['conv2_block2_3_conv[0][0]']
ization)
conv2_block2_add (Add) (None, 56, 56, 256) 0 ['conv2_block1_out[0][0]',
'conv2_block2_3_bn[0][0]']
conv2_block2_out (Activation) (None, 56, 56, 256) 0 ['conv2_block2_add[0][0]']
conv2_block3_1_conv (Conv2D) (None, 56, 56, 64) 16448 ['conv2_block2_out[0][0]']
conv2_block3_1_bn (BatchNormal (None, 56, 56, 64) 256 ['conv2_block3_1_conv[0][0]']
ization)
conv2_block3_1_relu (Activatio (None, 56, 56, 64) 0 ['conv2_block3_1_bn[0][0]']
n)
conv2_block3_2_conv (Conv2D) (None, 56, 56, 64) 36928 ['conv2_block3_1_relu[0][0]']
conv2_block3_2_bn (BatchNormal (None, 56, 56, 64) 256 ['conv2_block3_2_conv[0][0]']
ization)
conv2_block3_2_relu (Activatio (None, 56, 56, 64) 0 ['conv2_block3_2_bn[0][0]']
n)
conv2_block3_3_conv (Conv2D) (None, 56, 56, 256) 16640 ['conv2_block3_2_relu[0][0]']
conv2_block3_3_bn (BatchNormal (None, 56, 56, 256) 1024 ['conv2_block3_3_conv[0][0]']
ization)
conv2_block3_add (Add) (None, 56, 56, 256) 0 ['conv2_block2_out[0][0]',
'conv2_block3_3_bn[0][0]']
conv2_block3_out (Activation) (None, 56, 56, 256) 0 ['conv2_block3_add[0][0]']
conv3_block1_1_conv (Conv2D) (None, 28, 28, 128) 32896 ['conv2_block3_out[0][0]']
conv3_block1_1_bn (BatchNormal (None, 28, 28, 128) 512 ['conv3_block1_1_conv[0][0]']
ization)
conv3_block1_1_relu (Activatio (None, 28, 28, 128) 0 ['conv3_block1_1_bn[0][0]']
n)
conv3_block1_2_conv (Conv2D) (None, 28, 28, 128) 147584 ['conv3_block1_1_relu[0][0]']
conv3_block1_2_bn (BatchNormal (None, 28, 28, 128) 512 ['conv3_block1_2_conv[0][0]']
ization)
conv3_block1_2_relu (Activatio (None, 28, 28, 128) 0 ['conv3_block1_2_bn[0][0]']
n)
conv3_block1_0_conv (Conv2D) (None, 28, 28, 512) 131584 ['conv2_block3_out[0][0]']
conv3_block1_3_conv (Conv2D) (None, 28, 28, 512) 66048 ['conv3_block1_2_relu[0][0]']
conv3_block1_0_bn (BatchNormal (None, 28, 28, 512) 2048 ['conv3_block1_0_conv[0][0]']
ization)
conv3_block1_3_bn (BatchNormal (None, 28, 28, 512) 2048 ['conv3_block1_3_conv[0][0]']
ization)
conv3_block1_add (Add) (None, 28, 28, 512) 0 ['conv3_block1_0_bn[0][0]',
'conv3_block1_3_bn[0][0]']
conv3_block1_out (Activation) (None, 28, 28, 512) 0 ['conv3_block1_add[0][0]']
conv3_block2_1_conv (Conv2D) (None, 28, 28, 128) 65664 ['conv3_block1_out[0][0]']
conv3_block2_1_bn (BatchNormal (None, 28, 28, 128) 512 ['conv3_block2_1_conv[0][0]']
ization)
conv3_block2_1_relu (Activatio (None, 28, 28, 128) 0 ['conv3_block2_1_bn[0][0]']
n)
conv3_block2_2_conv (Conv2D) (None, 28, 28, 128) 147584 ['conv3_block2_1_relu[0][0]']
conv3_block2_2_bn (BatchNormal (None, 28, 28, 128) 512 ['conv3_block2_2_conv[0][0]']
ization)
conv3_block2_2_relu (Activatio (None, 28, 28, 128) 0 ['conv3_block2_2_bn[0][0]']
n)
conv3_block2_3_conv (Conv2D) (None, 28, 28, 512) 66048 ['conv3_block2_2_relu[0][0]']
conv3_block2_3_bn (BatchNormal (None, 28, 28, 512) 2048 ['conv3_block2_3_conv[0][0]']
ization)
conv3_block2_add (Add) (None, 28, 28, 512) 0 ['conv3_block1_out[0][0]',
'conv3_block2_3_bn[0][0]']
conv3_block2_out (Activation) (None, 28, 28, 512) 0 ['conv3_block2_add[0][0]']
conv3_block3_1_conv (Conv2D) (None, 28, 28, 128) 65664 ['conv3_block2_out[0][0]']
conv3_block3_1_bn (BatchNormal (None, 28, 28, 128) 512 ['conv3_block3_1_conv[0][0]']
ization)
conv3_block3_1_relu (Activatio (None, 28, 28, 128) 0 ['conv3_block3_1_bn[0][0]']
n)
conv3_block3_2_conv (Conv2D) (None, 28, 28, 128) 147584 ['conv3_block3_1_relu[0][0]']
conv3_block3_2_bn (BatchNormal (None, 28, 28, 128) 512 ['conv3_block3_2_conv[0][0]']
ization)
conv3_block3_2_relu (Activatio (None, 28, 28, 128) 0 ['conv3_block3_2_bn[0][0]']
n)
conv3_block3_3_conv (Conv2D) (None, 28, 28, 512) 66048 ['conv3_block3_2_relu[0][0]']
conv3_block3_3_bn (BatchNormal (None, 28, 28, 512) 2048 ['conv3_block3_3_conv[0][0]']
ization)
conv3_block3_add (Add) (None, 28, 28, 512) 0 ['conv3_block2_out[0][0]',
'conv3_block3_3_bn[0][0]']
conv3_block3_out (Activation) (None, 28, 28, 512) 0 ['conv3_block3_add[0][0]']
conv3_block4_1_conv (Conv2D) (None, 28, 28, 128) 65664 ['conv3_block3_out[0][0]']
conv3_block4_1_bn (BatchNormal (None, 28, 28, 128) 512 ['conv3_block4_1_conv[0][0]']
ization)
conv3_block4_1_relu (Activatio (None, 28, 28, 128) 0 ['conv3_block4_1_bn[0][0]']
n)
conv3_block4_2_conv (Conv2D) (None, 28, 28, 128) 147584 ['conv3_block4_1_relu[0][0]']
conv3_block4_2_bn (BatchNormal (None, 28, 28, 128) 512 ['conv3_block4_2_conv[0][0]']
ization)
conv3_block4_2_relu (Activatio (None, 28, 28, 128) 0 ['conv3_block4_2_bn[0][0]']
n)
conv3_block4_3_conv (Conv2D) (None, 28, 28, 512) 66048 ['conv3_block4_2_relu[0][0]']
conv3_block4_3_bn (BatchNormal (None, 28, 28, 512) 2048 ['conv3_block4_3_conv[0][0]']
ization)
conv3_block4_add (Add) (None, 28, 28, 512) 0 ['conv3_block3_out[0][0]',
'conv3_block4_3_bn[0][0]']
conv3_block4_out (Activation) (None, 28, 28, 512) 0 ['conv3_block4_add[0][0]']
conv4_block1_1_conv (Conv2D) (None, 14, 14, 256) 131328 ['conv3_block4_out[0][0]']
conv4_block1_1_bn (BatchNormal (None, 14, 14, 256) 1024 ['conv4_block1_1_conv[0][0]']
ization)
conv4_block1_1_relu (Activatio (None, 14, 14, 256) 0 ['conv4_block1_1_bn[0][0]']
n)
conv4_block1_2_conv (Conv2D) (None, 14, 14, 256) 590080 ['conv4_block1_1_relu[0][0]']
conv4_block1_2_bn (BatchNormal (None, 14, 14, 256) 1024 ['conv4_block1_2_conv[0][0]']
ization)
conv4_block1_2_relu (Activatio (None, 14, 14, 256) 0 ['conv4_block1_2_bn[0][0]']
n)
conv4_block1_0_conv (Conv2D) (None, 14, 14, 1024 525312 ['conv3_block4_out[0][0]']
)
conv4_block1_3_conv (Conv2D) (None, 14, 14, 1024 263168 ['conv4_block1_2_relu[0][0]']
)
conv4_block1_0_bn (BatchNormal (None, 14, 14, 1024 4096 ['conv4_block1_0_conv[0][0]']
ization) )
conv4_block1_3_bn (BatchNormal (None, 14, 14, 1024 4096 ['conv4_block1_3_conv[0][0]']
ization) )
conv4_block1_add (Add) (None, 14, 14, 1024 0 ['conv4_block1_0_bn[0][0]',
) 'conv4_block1_3_bn[0][0]']
conv4_block1_out (Activation) (None, 14, 14, 1024 0 ['conv4_block1_add[0][0]']
)
conv4_block2_1_conv (Conv2D) (None, 14, 14, 256) 262400 ['conv4_block1_out[0][0]']
conv4_block2_1_bn (BatchNormal (None, 14, 14, 256) 1024 ['conv4_block2_1_conv[0][0]']
ization)
conv4_block2_1_relu (Activatio (None, 14, 14, 256) 0 ['conv4_block2_1_bn[0][0]']
n)
conv4_block2_2_conv (Conv2D) (None, 14, 14, 256) 590080 ['conv4_block2_1_relu[0][0]']
conv4_block2_2_bn (BatchNormal (None, 14, 14, 256) 1024 ['conv4_block2_2_conv[0][0]']
ization)
conv4_block2_2_relu (Activatio (None, 14, 14, 256) 0 ['conv4_block2_2_bn[0][0]']
n)
conv4_block2_3_conv (Conv2D) (None, 14, 14, 1024 263168 ['conv4_block2_2_relu[0][0]']
)
conv4_block2_3_bn (BatchNormal (None, 14, 14, 1024 4096 ['conv4_block2_3_conv[0][0]']
ization) )
conv4_block2_add (Add) (None, 14, 14, 1024 0 ['conv4_block1_out[0][0]',
) 'conv4_block2_3_bn[0][0]']
conv4_block2_out (Activation) (None, 14, 14, 1024 0 ['conv4_block2_add[0][0]']
)
conv4_block3_1_conv (Conv2D) (None, 14, 14, 256) 262400 ['conv4_block2_out[0][0]']
conv4_block3_1_bn (BatchNormal (None, 14, 14, 256) 1024 ['conv4_block3_1_conv[0][0]']
ization)
conv4_block3_1_relu (Activatio (None, 14, 14, 256) 0 ['conv4_block3_1_bn[0][0]']
n)
conv4_block3_2_conv (Conv2D) (None, 14, 14, 256) 590080 ['conv4_block3_1_relu[0][0]']
conv4_block3_2_bn (BatchNormal (None, 14, 14, 256) 1024 ['conv4_block3_2_conv[0][0]']
ization)
conv4_block3_2_relu (Activatio (None, 14, 14, 256) 0 ['conv4_block3_2_bn[0][0]']
n)
conv4_block3_3_conv (Conv2D) (None, 14, 14, 1024 263168 ['conv4_block3_2_relu[0][0]']
)
conv4_block3_3_bn (BatchNormal (None, 14, 14, 1024 4096 ['conv4_block3_3_conv[0][0]']
ization) )
conv4_block3_add (Add) (None, 14, 14, 1024 0 ['conv4_block2_out[0][0]',
) 'conv4_block3_3_bn[0][0]']
conv4_block3_out (Activation) (None, 14, 14, 1024 0 ['conv4_block3_add[0][0]']
)
conv4_block4_1_conv (Conv2D) (None, 14, 14, 256) 262400 ['conv4_block3_out[0][0]']
conv4_block4_1_bn (BatchNormal (None, 14, 14, 256) 1024 ['conv4_block4_1_conv[0][0]']
ization)
conv4_block4_1_relu (Activatio (None, 14, 14, 256) 0 ['conv4_block4_1_bn[0][0]']
n)
conv4_block4_2_conv (Conv2D) (None, 14, 14, 256) 590080 ['conv4_block4_1_relu[0][0]']
conv4_block4_2_bn (BatchNormal (None, 14, 14, 256) 1024 ['conv4_block4_2_conv[0][0]']
ization)
conv4_block4_2_relu (Activatio (None, 14, 14, 256) 0 ['conv4_block4_2_bn[0][0]']
n)
conv4_block4_3_conv (Conv2D) (None, 14, 14, 1024 263168 ['conv4_block4_2_relu[0][0]']
)
conv4_block4_3_bn (BatchNormal (None, 14, 14, 1024 4096 ['conv4_block4_3_conv[0][0]']
ization) )
conv4_block4_add (Add) (None, 14, 14, 1024 0 ['conv4_block3_out[0][0]',
) 'conv4_block4_3_bn[0][0]']
conv4_block4_out (Activation) (None, 14, 14, 1024 0 ['conv4_block4_add[0][0]']
)
conv4_block5_1_conv (Conv2D) (None, 14, 14, 256) 262400 ['conv4_block4_out[0][0]']
conv4_block5_1_bn (BatchNormal (None, 14, 14, 256) 1024 ['conv4_block5_1_conv[0][0]']
ization)
conv4_block5_1_relu (Activatio (None, 14, 14, 256) 0 ['conv4_block5_1_bn[0][0]']
n)
conv4_block5_2_conv (Conv2D) (None, 14, 14, 256) 590080 ['conv4_block5_1_relu[0][0]']
conv4_block5_2_bn (BatchNormal (None, 14, 14, 256) 1024 ['conv4_block5_2_conv[0][0]']
ization)
conv4_block5_2_relu (Activatio (None, 14, 14, 256) 0 ['conv4_block5_2_bn[0][0]']
n)
conv4_block5_3_conv (Conv2D) (None, 14, 14, 1024 263168 ['conv4_block5_2_relu[0][0]']
)
conv4_block5_3_bn (BatchNormal (None, 14, 14, 1024 4096 ['conv4_block5_3_conv[0][0]']
ization) )
conv4_block5_add (Add) (None, 14, 14, 1024 0 ['conv4_block4_out[0][0]',
) 'conv4_block5_3_bn[0][0]']
conv4_block5_out (Activation) (None, 14, 14, 1024 0 ['conv4_block5_add[0][0]']
)
conv4_block6_1_conv (Conv2D) (None, 14, 14, 256) 262400 ['conv4_block5_out[0][0]']
conv4_block6_1_bn (BatchNormal (None, 14, 14, 256) 1024 ['conv4_block6_1_conv[0][0]']
ization)
conv4_block6_1_relu (Activatio (None, 14, 14, 256) 0 ['conv4_block6_1_bn[0][0]']
n)
conv4_block6_2_conv (Conv2D) (None, 14, 14, 256) 590080 ['conv4_block6_1_relu[0][0]']
conv4_block6_2_bn (BatchNormal (None, 14, 14, 256) 1024 ['conv4_block6_2_conv[0][0]']
ization)
conv4_block6_2_relu (Activatio (None, 14, 14, 256) 0 ['conv4_block6_2_bn[0][0]']
n)
conv4_block6_3_conv (Conv2D) (None, 14, 14, 1024 263168 ['conv4_block6_2_relu[0][0]']
)
conv4_block6_3_bn (BatchNormal (None, 14, 14, 1024 4096 ['conv4_block6_3_conv[0][0]']
ization) )
conv4_block6_add (Add) (None, 14, 14, 1024 0 ['conv4_block5_out[0][0]',
) 'conv4_block6_3_bn[0][0]']
conv4_block6_out (Activation) (None, 14, 14, 1024 0 ['conv4_block6_add[0][0]']
)
conv5_block1_1_conv (Conv2D) (None, 7, 7, 512) 524800 ['conv4_block6_out[0][0]']
conv5_block1_1_bn (BatchNormal (None, 7, 7, 512) 2048 ['conv5_block1_1_conv[0][0]']
ization)
conv5_block1_1_relu (Activatio (None, 7, 7, 512) 0 ['conv5_block1_1_bn[0][0]']
n)
conv5_block1_2_conv (Conv2D) (None, 7, 7, 512) 2359808 ['conv5_block1_1_relu[0][0]']
conv5_block1_2_bn (BatchNormal (None, 7, 7, 512) 2048 ['conv5_block1_2_conv[0][0]']
ization)
conv5_block1_2_relu (Activatio (None, 7, 7, 512) 0 ['conv5_block1_2_bn[0][0]']
n)
conv5_block1_0_conv (Conv2D) (None, 7, 7, 2048) 2099200 ['conv4_block6_out[0][0]']
conv5_block1_3_conv (Conv2D) (None, 7, 7, 2048) 1050624 ['conv5_block1_2_relu[0][0]']
conv5_block1_0_bn (BatchNormal (None, 7, 7, 2048) 8192 ['conv5_block1_0_conv[0][0]']
ization)
conv5_block1_3_bn (BatchNormal (None, 7, 7, 2048) 8192 ['conv5_block1_3_conv[0][0]']
ization)
conv5_block1_add (Add) (None, 7, 7, 2048) 0 ['conv5_block1_0_bn[0][0]',
'conv5_block1_3_bn[0][0]']
conv5_block1_out (Activation) (None, 7, 7, 2048) 0 ['conv5_block1_add[0][0]']
conv5_block2_1_conv (Conv2D) (None, 7, 7, 512) 1049088 ['conv5_block1_out[0][0]']
conv5_block2_1_bn (BatchNormal (None, 7, 7, 512) 2048 ['conv5_block2_1_conv[0][0]']
ization)
conv5_block2_1_relu (Activatio (None, 7, 7, 512) 0 ['conv5_block2_1_bn[0][0]']
n)
conv5_block2_2_conv (Conv2D) (None, 7, 7, 512) 2359808 ['conv5_block2_1_relu[0][0]']
conv5_block2_2_bn (BatchNormal (None, 7, 7, 512) 2048 ['conv5_block2_2_conv[0][0]']
ization)
conv5_block2_2_relu (Activatio (None, 7, 7, 512) 0 ['conv5_block2_2_bn[0][0]']
n)
conv5_block2_3_conv (Conv2D) (None, 7, 7, 2048) 1050624 ['conv5_block2_2_relu[0][0]']
conv5_block2_3_bn (BatchNormal (None, 7, 7, 2048) 8192 ['conv5_block2_3_conv[0][0]']
ization)
conv5_block2_add (Add) (None, 7, 7, 2048) 0 ['conv5_block1_out[0][0]',
'conv5_block2_3_bn[0][0]']
conv5_block2_out (Activation) (None, 7, 7, 2048) 0 ['conv5_block2_add[0][0]']
conv5_block3_1_conv (Conv2D) (None, 7, 7, 512) 1049088 ['conv5_block2_out[0][0]']
conv5_block3_1_bn (BatchNormal (None, 7, 7, 512) 2048 ['conv5_block3_1_conv[0][0]']
ization)
conv5_block3_1_relu (Activatio (None, 7, 7, 512) 0 ['conv5_block3_1_bn[0][0]']
n)
conv5_block3_2_conv (Conv2D) (None, 7, 7, 512) 2359808 ['conv5_block3_1_relu[0][0]']
conv5_block3_2_bn (BatchNormal (None, 7, 7, 512) 2048 ['conv5_block3_2_conv[0][0]']
ization)
conv5_block3_2_relu (Activatio (None, 7, 7, 512) 0 ['conv5_block3_2_bn[0][0]']
n)
conv5_block3_3_conv (Conv2D) (None, 7, 7, 2048) 1050624 ['conv5_block3_2_relu[0][0]']
conv5_block3_3_bn (BatchNormal (None, 7, 7, 2048) 8192 ['conv5_block3_3_conv[0][0]']
ization)
conv5_block3_add (Add) (None, 7, 7, 2048) 0 ['conv5_block2_out[0][0]',
'conv5_block3_3_bn[0][0]']
conv5_block3_out (Activation) (None, 7, 7, 2048) 0 ['conv5_block3_add[0][0]']
avg_pool (GlobalAveragePooling (None, 2048) 0 ['conv5_block3_out[0][0]']
2D)
predictions (Dense) (None, 1000) 2049000 ['avg_pool[0][0]']
==================================================================================================
Total params: 25,636,712
Trainable params: 25,583,592
Non-trainable params: 53,120
__________________________________________________________________________________________________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,
)
model <- application_resnet50(weights = "imagenet")
summary(model)Model: "resnet50"
________________________________________________________________________________
Layer (type) Output Shape Param # Connected to Trainable
================================================================================
input_2 (InputLayer) [(None, 224, 0 [] Y
224, 3)]
conv1_pad (ZeroPaddi (None, 230, 2 0 ['input_2[0][0]'] Y
ng2D) 30, 3)
conv1_conv (Conv2D) (None, 112, 1 9472 ['conv1_pad[0][0]'] Y
12, 64)
conv1_bn (BatchNorma (None, 112, 1 256 ['conv1_conv[0][0]'] Y
lization) 12, 64)
conv1_relu (Activati (None, 112, 1 0 ['conv1_bn[0][0]'] Y
on) 12, 64)
pool1_pad (ZeroPaddi (None, 114, 1 0 ['conv1_relu[0][0]'] Y
ng2D) 14, 64)
pool1_pool (MaxPooli (None, 56, 56 0 ['pool1_pad[0][0]'] Y
ng2D) , 64)
conv2_block1_1_conv (None, 56, 56 4160 ['pool1_pool[0][0]'] Y
(Conv2D) , 64)
conv2_block1_1_bn (B (None, 56, 56 256 ['conv2_block1_1_conv Y
atchNormalization) , 64) [0][0]']
conv2_block1_1_relu (None, 56, 56 0 ['conv2_block1_1_bn[0 Y
(Activation) , 64) ][0]']
conv2_block1_2_conv (None, 56, 56 36928 ['conv2_block1_1_relu Y
(Conv2D) , 64) [0][0]']
conv2_block1_2_bn (B (None, 56, 56 256 ['conv2_block1_2_conv Y
atchNormalization) , 64) [0][0]']
conv2_block1_2_relu (None, 56, 56 0 ['conv2_block1_2_bn[0 Y
(Activation) , 64) ][0]']
conv2_block1_0_conv (None, 56, 56 16640 ['pool1_pool[0][0]'] Y
(Conv2D) , 256)
conv2_block1_3_conv (None, 56, 56 16640 ['conv2_block1_2_relu Y
(Conv2D) , 256) [0][0]']
conv2_block1_0_bn (B (None, 56, 56 1024 ['conv2_block1_0_conv Y
atchNormalization) , 256) [0][0]']
conv2_block1_3_bn (B (None, 56, 56 1024 ['conv2_block1_3_conv Y
atchNormalization) , 256) [0][0]']
conv2_block1_add (Ad (None, 56, 56 0 ['conv2_block1_0_bn[0 Y
d) , 256) ][0]',
'conv2_block1_3_bn[0
][0]']
conv2_block1_out (Ac (None, 56, 56 0 ['conv2_block1_add[0] Y
tivation) , 256) [0]']
conv2_block2_1_conv (None, 56, 56 16448 ['conv2_block1_out[0] Y
(Conv2D) , 64) [0]']
conv2_block2_1_bn (B (None, 56, 56 256 ['conv2_block2_1_conv Y
atchNormalization) , 64) [0][0]']
conv2_block2_1_relu (None, 56, 56 0 ['conv2_block2_1_bn[0 Y
(Activation) , 64) ][0]']
conv2_block2_2_conv (None, 56, 56 36928 ['conv2_block2_1_relu Y
(Conv2D) , 64) [0][0]']
conv2_block2_2_bn (B (None, 56, 56 256 ['conv2_block2_2_conv Y
atchNormalization) , 64) [0][0]']
conv2_block2_2_relu (None, 56, 56 0 ['conv2_block2_2_bn[0 Y
(Activation) , 64) ][0]']
conv2_block2_3_conv (None, 56, 56 16640 ['conv2_block2_2_relu Y
(Conv2D) , 256) [0][0]']
conv2_block2_3_bn (B (None, 56, 56 1024 ['conv2_block2_3_conv Y
atchNormalization) , 256) [0][0]']
conv2_block2_add (Ad (None, 56, 56 0 ['conv2_block1_out[0] Y
d) , 256) [0]',
'conv2_block2_3_bn[0
][0]']
conv2_block2_out (Ac (None, 56, 56 0 ['conv2_block2_add[0] Y
tivation) , 256) [0]']
conv2_block3_1_conv (None, 56, 56 16448 ['conv2_block2_out[0] Y
(Conv2D) , 64) [0]']
conv2_block3_1_bn (B (None, 56, 56 256 ['conv2_block3_1_conv Y
atchNormalization) , 64) [0][0]']
conv2_block3_1_relu (None, 56, 56 0 ['conv2_block3_1_bn[0 Y
(Activation) , 64) ][0]']
conv2_block3_2_conv (None, 56, 56 36928 ['conv2_block3_1_relu Y
(Conv2D) , 64) [0][0]']
conv2_block3_2_bn (B (None, 56, 56 256 ['conv2_block3_2_conv Y
atchNormalization) , 64) [0][0]']
conv2_block3_2_relu (None, 56, 56 0 ['conv2_block3_2_bn[0 Y
(Activation) , 64) ][0]']
conv2_block3_3_conv (None, 56, 56 16640 ['conv2_block3_2_relu Y
(Conv2D) , 256) [0][0]']
conv2_block3_3_bn (B (None, 56, 56 1024 ['conv2_block3_3_conv Y
atchNormalization) , 256) [0][0]']
conv2_block3_add (Ad (None, 56, 56 0 ['conv2_block2_out[0] Y
d) , 256) [0]',
'conv2_block3_3_bn[0
][0]']
conv2_block3_out (Ac (None, 56, 56 0 ['conv2_block3_add[0] Y
tivation) , 256) [0]']
conv3_block1_1_conv (None, 28, 28 32896 ['conv2_block3_out[0] Y
(Conv2D) , 128) [0]']
conv3_block1_1_bn (B (None, 28, 28 512 ['conv3_block1_1_conv Y
atchNormalization) , 128) [0][0]']
conv3_block1_1_relu (None, 28, 28 0 ['conv3_block1_1_bn[0 Y
(Activation) , 128) ][0]']
conv3_block1_2_conv (None, 28, 28 147584 ['conv3_block1_1_relu Y
(Conv2D) , 128) [0][0]']
conv3_block1_2_bn (B (None, 28, 28 512 ['conv3_block1_2_conv Y
atchNormalization) , 128) [0][0]']
conv3_block1_2_relu (None, 28, 28 0 ['conv3_block1_2_bn[0 Y
(Activation) , 128) ][0]']
conv3_block1_0_conv (None, 28, 28 131584 ['conv2_block3_out[0] Y
(Conv2D) , 512) [0]']
conv3_block1_3_conv (None, 28, 28 66048 ['conv3_block1_2_relu Y
(Conv2D) , 512) [0][0]']
conv3_block1_0_bn (B (None, 28, 28 2048 ['conv3_block1_0_conv Y
atchNormalization) , 512) [0][0]']
conv3_block1_3_bn (B (None, 28, 28 2048 ['conv3_block1_3_conv Y
atchNormalization) , 512) [0][0]']
conv3_block1_add (Ad (None, 28, 28 0 ['conv3_block1_0_bn[0 Y
d) , 512) ][0]',
'conv3_block1_3_bn[0
][0]']
conv3_block1_out (Ac (None, 28, 28 0 ['conv3_block1_add[0] Y
tivation) , 512) [0]']
conv3_block2_1_conv (None, 28, 28 65664 ['conv3_block1_out[0] Y
(Conv2D) , 128) [0]']
conv3_block2_1_bn (B (None, 28, 28 512 ['conv3_block2_1_conv Y
atchNormalization) , 128) [0][0]']
conv3_block2_1_relu (None, 28, 28 0 ['conv3_block2_1_bn[0 Y
(Activation) , 128) ][0]']
conv3_block2_2_conv (None, 28, 28 147584 ['conv3_block2_1_relu Y
(Conv2D) , 128) [0][0]']
conv3_block2_2_bn (B (None, 28, 28 512 ['conv3_block2_2_conv Y
atchNormalization) , 128) [0][0]']
conv3_block2_2_relu (None, 28, 28 0 ['conv3_block2_2_bn[0 Y
(Activation) , 128) ][0]']
conv3_block2_3_conv (None, 28, 28 66048 ['conv3_block2_2_relu Y
(Conv2D) , 512) [0][0]']
conv3_block2_3_bn (B (None, 28, 28 2048 ['conv3_block2_3_conv Y
atchNormalization) , 512) [0][0]']
conv3_block2_add (Ad (None, 28, 28 0 ['conv3_block1_out[0] Y
d) , 512) [0]',
'conv3_block2_3_bn[0
][0]']
conv3_block2_out (Ac (None, 28, 28 0 ['conv3_block2_add[0] Y
tivation) , 512) [0]']
conv3_block3_1_conv (None, 28, 28 65664 ['conv3_block2_out[0] Y
(Conv2D) , 128) [0]']
conv3_block3_1_bn (B (None, 28, 28 512 ['conv3_block3_1_conv Y
atchNormalization) , 128) [0][0]']
conv3_block3_1_relu (None, 28, 28 0 ['conv3_block3_1_bn[0 Y
(Activation) , 128) ][0]']
conv3_block3_2_conv (None, 28, 28 147584 ['conv3_block3_1_relu Y
(Conv2D) , 128) [0][0]']
conv3_block3_2_bn (B (None, 28, 28 512 ['conv3_block3_2_conv Y
atchNormalization) , 128) [0][0]']
conv3_block3_2_relu (None, 28, 28 0 ['conv3_block3_2_bn[0 Y
(Activation) , 128) ][0]']
conv3_block3_3_conv (None, 28, 28 66048 ['conv3_block3_2_relu Y
(Conv2D) , 512) [0][0]']
conv3_block3_3_bn (B (None, 28, 28 2048 ['conv3_block3_3_conv Y
atchNormalization) , 512) [0][0]']
conv3_block3_add (Ad (None, 28, 28 0 ['conv3_block2_out[0] Y
d) , 512) [0]',
'conv3_block3_3_bn[0
][0]']
conv3_block3_out (Ac (None, 28, 28 0 ['conv3_block3_add[0] Y
tivation) , 512) [0]']
conv3_block4_1_conv (None, 28, 28 65664 ['conv3_block3_out[0] Y
(Conv2D) , 128) [0]']
conv3_block4_1_bn (B (None, 28, 28 512 ['conv3_block4_1_conv Y
atchNormalization) , 128) [0][0]']
conv3_block4_1_relu (None, 28, 28 0 ['conv3_block4_1_bn[0 Y
(Activation) , 128) ][0]']
conv3_block4_2_conv (None, 28, 28 147584 ['conv3_block4_1_relu Y
(Conv2D) , 128) [0][0]']
conv3_block4_2_bn (B (None, 28, 28 512 ['conv3_block4_2_conv Y
atchNormalization) , 128) [0][0]']
conv3_block4_2_relu (None, 28, 28 0 ['conv3_block4_2_bn[0 Y
(Activation) , 128) ][0]']
conv3_block4_3_conv (None, 28, 28 66048 ['conv3_block4_2_relu Y
(Conv2D) , 512) [0][0]']
conv3_block4_3_bn (B (None, 28, 28 2048 ['conv3_block4_3_conv Y
atchNormalization) , 512) [0][0]']
conv3_block4_add (Ad (None, 28, 28 0 ['conv3_block3_out[0] Y
d) , 512) [0]',
'conv3_block4_3_bn[0
][0]']
conv3_block4_out (Ac (None, 28, 28 0 ['conv3_block4_add[0] Y
tivation) , 512) [0]']
conv4_block1_1_conv (None, 14, 14 131328 ['conv3_block4_out[0] Y
(Conv2D) , 256) [0]']
conv4_block1_1_bn (B (None, 14, 14 1024 ['conv4_block1_1_conv Y
atchNormalization) , 256) [0][0]']
conv4_block1_1_relu (None, 14, 14 0 ['conv4_block1_1_bn[0 Y
(Activation) , 256) ][0]']
conv4_block1_2_conv (None, 14, 14 590080 ['conv4_block1_1_relu Y
(Conv2D) , 256) [0][0]']
conv4_block1_2_bn (B (None, 14, 14 1024 ['conv4_block1_2_conv Y
atchNormalization) , 256) [0][0]']
conv4_block1_2_relu (None, 14, 14 0 ['conv4_block1_2_bn[0 Y
(Activation) , 256) ][0]']
conv4_block1_0_conv (None, 14, 14 525312 ['conv3_block4_out[0] Y
(Conv2D) , 1024) [0]']
conv4_block1_3_conv (None, 14, 14 263168 ['conv4_block1_2_relu Y
(Conv2D) , 1024) [0][0]']
conv4_block1_0_bn (B (None, 14, 14 4096 ['conv4_block1_0_conv Y
atchNormalization) , 1024) [0][0]']
conv4_block1_3_bn (B (None, 14, 14 4096 ['conv4_block1_3_conv Y
atchNormalization) , 1024) [0][0]']
conv4_block1_add (Ad (None, 14, 14 0 ['conv4_block1_0_bn[0 Y
d) , 1024) ][0]',
'conv4_block1_3_bn[0
][0]']
conv4_block1_out (Ac (None, 14, 14 0 ['conv4_block1_add[0] Y
tivation) , 1024) [0]']
conv4_block2_1_conv (None, 14, 14 262400 ['conv4_block1_out[0] Y
(Conv2D) , 256) [0]']
conv4_block2_1_bn (B (None, 14, 14 1024 ['conv4_block2_1_conv Y
atchNormalization) , 256) [0][0]']
conv4_block2_1_relu (None, 14, 14 0 ['conv4_block2_1_bn[0 Y
(Activation) , 256) ][0]']
conv4_block2_2_conv (None, 14, 14 590080 ['conv4_block2_1_relu Y
(Conv2D) , 256) [0][0]']
conv4_block2_2_bn (B (None, 14, 14 1024 ['conv4_block2_2_conv Y
atchNormalization) , 256) [0][0]']
conv4_block2_2_relu (None, 14, 14 0 ['conv4_block2_2_bn[0 Y
(Activation) , 256) ][0]']
conv4_block2_3_conv (None, 14, 14 263168 ['conv4_block2_2_relu Y
(Conv2D) , 1024) [0][0]']
conv4_block2_3_bn (B (None, 14, 14 4096 ['conv4_block2_3_conv Y
atchNormalization) , 1024) [0][0]']
conv4_block2_add (Ad (None, 14, 14 0 ['conv4_block1_out[0] Y
d) , 1024) [0]',
'conv4_block2_3_bn[0
][0]']
conv4_block2_out (Ac (None, 14, 14 0 ['conv4_block2_add[0] Y
tivation) , 1024) [0]']
conv4_block3_1_conv (None, 14, 14 262400 ['conv4_block2_out[0] Y
(Conv2D) , 256) [0]']
conv4_block3_1_bn (B (None, 14, 14 1024 ['conv4_block3_1_conv Y
atchNormalization) , 256) [0][0]']
conv4_block3_1_relu (None, 14, 14 0 ['conv4_block3_1_bn[0 Y
(Activation) , 256) ][0]']
conv4_block3_2_conv (None, 14, 14 590080 ['conv4_block3_1_relu Y
(Conv2D) , 256) [0][0]']
conv4_block3_2_bn (B (None, 14, 14 1024 ['conv4_block3_2_conv Y
atchNormalization) , 256) [0][0]']
conv4_block3_2_relu (None, 14, 14 0 ['conv4_block3_2_bn[0 Y
(Activation) , 256) ][0]']
conv4_block3_3_conv (None, 14, 14 263168 ['conv4_block3_2_relu Y
(Conv2D) , 1024) [0][0]']
conv4_block3_3_bn (B (None, 14, 14 4096 ['conv4_block3_3_conv Y
atchNormalization) , 1024) [0][0]']
conv4_block3_add (Ad (None, 14, 14 0 ['conv4_block2_out[0] Y
d) , 1024) [0]',
'conv4_block3_3_bn[0
][0]']
conv4_block3_out (Ac (None, 14, 14 0 ['conv4_block3_add[0] Y
tivation) , 1024) [0]']
conv4_block4_1_conv (None, 14, 14 262400 ['conv4_block3_out[0] Y
(Conv2D) , 256) [0]']
conv4_block4_1_bn (B (None, 14, 14 1024 ['conv4_block4_1_conv Y
atchNormalization) , 256) [0][0]']
conv4_block4_1_relu (None, 14, 14 0 ['conv4_block4_1_bn[0 Y
(Activation) , 256) ][0]']
conv4_block4_2_conv (None, 14, 14 590080 ['conv4_block4_1_relu Y
(Conv2D) , 256) [0][0]']
conv4_block4_2_bn (B (None, 14, 14 1024 ['conv4_block4_2_conv Y
atchNormalization) , 256) [0][0]']
conv4_block4_2_relu (None, 14, 14 0 ['conv4_block4_2_bn[0 Y
(Activation) , 256) ][0]']
conv4_block4_3_conv (None, 14, 14 263168 ['conv4_block4_2_relu Y
(Conv2D) , 1024) [0][0]']
conv4_block4_3_bn (B (None, 14, 14 4096 ['conv4_block4_3_conv Y
atchNormalization) , 1024) [0][0]']
conv4_block4_add (Ad (None, 14, 14 0 ['conv4_block3_out[0] Y
d) , 1024) [0]',
'conv4_block4_3_bn[0
][0]']
conv4_block4_out (Ac (None, 14, 14 0 ['conv4_block4_add[0] Y
tivation) , 1024) [0]']
conv4_block5_1_conv (None, 14, 14 262400 ['conv4_block4_out[0] Y
(Conv2D) , 256) [0]']
conv4_block5_1_bn (B (None, 14, 14 1024 ['conv4_block5_1_conv Y
atchNormalization) , 256) [0][0]']
conv4_block5_1_relu (None, 14, 14 0 ['conv4_block5_1_bn[0 Y
(Activation) , 256) ][0]']
conv4_block5_2_conv (None, 14, 14 590080 ['conv4_block5_1_relu Y
(Conv2D) , 256) [0][0]']
conv4_block5_2_bn (B (None, 14, 14 1024 ['conv4_block5_2_conv Y
atchNormalization) , 256) [0][0]']
conv4_block5_2_relu (None, 14, 14 0 ['conv4_block5_2_bn[0 Y
(Activation) , 256) ][0]']
conv4_block5_3_conv (None, 14, 14 263168 ['conv4_block5_2_relu Y
(Conv2D) , 1024) [0][0]']
conv4_block5_3_bn (B (None, 14, 14 4096 ['conv4_block5_3_conv Y
atchNormalization) , 1024) [0][0]']
conv4_block5_add (Ad (None, 14, 14 0 ['conv4_block4_out[0] Y
d) , 1024) [0]',
'conv4_block5_3_bn[0
][0]']
conv4_block5_out (Ac (None, 14, 14 0 ['conv4_block5_add[0] Y
tivation) , 1024) [0]']
conv4_block6_1_conv (None, 14, 14 262400 ['conv4_block5_out[0] Y
(Conv2D) , 256) [0]']
conv4_block6_1_bn (B (None, 14, 14 1024 ['conv4_block6_1_conv Y
atchNormalization) , 256) [0][0]']
conv4_block6_1_relu (None, 14, 14 0 ['conv4_block6_1_bn[0 Y
(Activation) , 256) ][0]']
conv4_block6_2_conv (None, 14, 14 590080 ['conv4_block6_1_relu Y
(Conv2D) , 256) [0][0]']
conv4_block6_2_bn (B (None, 14, 14 1024 ['conv4_block6_2_conv Y
atchNormalization) , 256) [0][0]']
conv4_block6_2_relu (None, 14, 14 0 ['conv4_block6_2_bn[0 Y
(Activation) , 256) ][0]']
conv4_block6_3_conv (None, 14, 14 263168 ['conv4_block6_2_relu Y
(Conv2D) , 1024) [0][0]']
conv4_block6_3_bn (B (None, 14, 14 4096 ['conv4_block6_3_conv Y
atchNormalization) , 1024) [0][0]']
conv4_block6_add (Ad (None, 14, 14 0 ['conv4_block5_out[0] Y
d) , 1024) [0]',
'conv4_block6_3_bn[0
][0]']
conv4_block6_out (Ac (None, 14, 14 0 ['conv4_block6_add[0] Y
tivation) , 1024) [0]']
conv5_block1_1_conv (None, 7, 7, 524800 ['conv4_block6_out[0] Y
(Conv2D) 512) [0]']
conv5_block1_1_bn (B (None, 7, 7, 2048 ['conv5_block1_1_conv Y
atchNormalization) 512) [0][0]']
conv5_block1_1_relu (None, 7, 7, 0 ['conv5_block1_1_bn[0 Y
(Activation) 512) ][0]']
conv5_block1_2_conv (None, 7, 7, 2359808 ['conv5_block1_1_relu Y
(Conv2D) 512) [0][0]']
conv5_block1_2_bn (B (None, 7, 7, 2048 ['conv5_block1_2_conv Y
atchNormalization) 512) [0][0]']
conv5_block1_2_relu (None, 7, 7, 0 ['conv5_block1_2_bn[0 Y
(Activation) 512) ][0]']
conv5_block1_0_conv (None, 7, 7, 2099200 ['conv4_block6_out[0] Y
(Conv2D) 2048) [0]']
conv5_block1_3_conv (None, 7, 7, 1050624 ['conv5_block1_2_relu Y
(Conv2D) 2048) [0][0]']
conv5_block1_0_bn (B (None, 7, 7, 8192 ['conv5_block1_0_conv Y
atchNormalization) 2048) [0][0]']
conv5_block1_3_bn (B (None, 7, 7, 8192 ['conv5_block1_3_conv Y
atchNormalization) 2048) [0][0]']
conv5_block1_add (Ad (None, 7, 7, 0 ['conv5_block1_0_bn[0 Y
d) 2048) ][0]',
'conv5_block1_3_bn[0
][0]']
conv5_block1_out (Ac (None, 7, 7, 0 ['conv5_block1_add[0] Y
tivation) 2048) [0]']
conv5_block2_1_conv (None, 7, 7, 1049088 ['conv5_block1_out[0] Y
(Conv2D) 512) [0]']
conv5_block2_1_bn (B (None, 7, 7, 2048 ['conv5_block2_1_conv Y
atchNormalization) 512) [0][0]']
conv5_block2_1_relu (None, 7, 7, 0 ['conv5_block2_1_bn[0 Y
(Activation) 512) ][0]']
conv5_block2_2_conv (None, 7, 7, 2359808 ['conv5_block2_1_relu Y
(Conv2D) 512) [0][0]']
conv5_block2_2_bn (B (None, 7, 7, 2048 ['conv5_block2_2_conv Y
atchNormalization) 512) [0][0]']
conv5_block2_2_relu (None, 7, 7, 0 ['conv5_block2_2_bn[0 Y
(Activation) 512) ][0]']
conv5_block2_3_conv (None, 7, 7, 1050624 ['conv5_block2_2_relu Y
(Conv2D) 2048) [0][0]']
conv5_block2_3_bn (B (None, 7, 7, 8192 ['conv5_block2_3_conv Y
atchNormalization) 2048) [0][0]']
conv5_block2_add (Ad (None, 7, 7, 0 ['conv5_block1_out[0] Y
d) 2048) [0]',
'conv5_block2_3_bn[0
][0]']
conv5_block2_out (Ac (None, 7, 7, 0 ['conv5_block2_add[0] Y
tivation) 2048) [0]']
conv5_block3_1_conv (None, 7, 7, 1049088 ['conv5_block2_out[0] Y
(Conv2D) 512) [0]']
conv5_block3_1_bn (B (None, 7, 7, 2048 ['conv5_block3_1_conv Y
atchNormalization) 512) [0][0]']
conv5_block3_1_relu (None, 7, 7, 0 ['conv5_block3_1_bn[0 Y
(Activation) 512) ][0]']
conv5_block3_2_conv (None, 7, 7, 2359808 ['conv5_block3_1_relu Y
(Conv2D) 512) [0][0]']
conv5_block3_2_bn (B (None, 7, 7, 2048 ['conv5_block3_2_conv Y
atchNormalization) 512) [0][0]']
conv5_block3_2_relu (None, 7, 7, 0 ['conv5_block3_2_bn[0 Y
(Activation) 512) ][0]']
conv5_block3_3_conv (None, 7, 7, 1050624 ['conv5_block3_2_relu Y
(Conv2D) 2048) [0][0]']
conv5_block3_3_bn (B (None, 7, 7, 8192 ['conv5_block3_3_conv Y
atchNormalization) 2048) [0][0]']
conv5_block3_add (Ad (None, 7, 7, 0 ['conv5_block2_out[0] Y
d) 2048) [0]',
'conv5_block3_3_bn[0
][0]']
conv5_block3_out (Ac (None, 7, 7, 0 ['conv5_block3_add[0] Y
tivation) 2048) [0]']
avg_pool (GlobalAver (None, 2048) 0 ['conv5_block3_out[0] Y
agePooling2D) [0]']
predictions (Dense) (None, 1000) 2049000 ['avg_pool[0][0]'] Y
================================================================================
Total params: 25,636,712
Trainable params: 25,583,592
Non-trainable params: 53,120
________________________________________________________________________________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 [==============================] - 1s 1s/stepfor i in range(num_images):
print(image_names[i])
print(np.asarray(pred6[i]))weaver.jpg
[['n01843065' 'jacamar' '0.4865981']
['n01818515' 'macaw' '0.23028952']
['n02494079' 'squirrel_monkey' '0.044571042']]
flamingo.jpg
[['n02007558' 'flamingo' '0.93010914']
['n02006656' 'spoonbill' '0.06801486']
['n02002556' 'white_stork' '0.0011723748']]
huey.jpg
[['n02097474' 'Tibetan_terrier' '0.5246206']
['n02098413' 'Lhasa' '0.40590215']
['n02094114' 'Norfolk_terrier' '0.016995324']]
hawk_cropped.jpeg
[['n01608432' 'kite' '0.7243426']
['n01622779' 'great_grey_owl' '0.086835146']
['n01532829' 'house_finch' '0.039976425']]
hawk.jpg
[['n03388043' 'fountain' '0.27721697']
['n03532672' 'hook' '0.17947067']
['n03804744' 'nail' '0.1103583']]
kitty.jpg
[['n02105641' 'Old_English_sheepdog' '0.83900243']
['n02086240' 'Shih-Tzu' '0.04235816']
['n03223299' 'doormat' '0.032265782']]pred6 <- model %>%
predict(x) %>%
imagenet_decode_predictions(top = 3)
names(pred6) <- image_names
pred6$flamingo.jpg
class_name class_description score
1 n02007558 flamingo 0.930109143
2 n02006656 spoonbill 0.068014860
3 n02002556 white_stork 0.001172375
$hawk_cropped.jpeg
class_name class_description score
1 n01608432 kite 0.72434247
2 n01622779 great_grey_owl 0.08683521
3 n01532829 house_finch 0.03997646
$hawk.jpg
class_name class_description score
1 n03388043 fountain 0.2772168
2 n03532672 hook 0.1794709
3 n03804744 nail 0.1103582
$huey.jpg
class_name class_description score
1 n02097474 Tibetan_terrier 0.52462149
2 n02098413 Lhasa 0.40590131
3 n02094114 Norfolk_terrier 0.01699529
$kitty.jpg
class_name class_description score
1 n02105641 Old_English_sheepdog 0.83900261
2 n02086240 Shih-Tzu 0.04235817
3 n03223299 doormat 0.03226573
$weaver.jpg
class_name class_description score
1 n01843065 jacamar 0.48659873
2 n01818515 macaw 0.23028916
3 n02494079 squirrel_monkey 0.04457097