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キカガク（KIKAGAKU）で「畳み込みニューラルネットワークの代表的なモデル」を学ぶ

Photo by Owen Beard on Unsplash

今回は「畳み込みニューラルネットワークの代表的なモデル - KIKAGAKU」を学んだ。

Table of Contents [Disable]

学習内容

Functional API

複数の入出力が発生するようなモデルの場合、Sequential API では記述できないため、Functional API を使って記述する。

VGGNet（2014）

VGGNet は非常にシンプルでわかりやすいアーキテクチャであるのに精度が高い。

VGGNet の特徴

  1. 3×3 フィルタのみを使用
  2. 同一チャネルの複数の畳み込み層と Max Pooling を 1 セットとし、繰り返す
  3. Max Pooling 後の出力チャネル数を 2 倍にする」

3×3 フィルタを使用するのは、上下左右中心の情報が受容できる最も小さなサイズであるため。

パラメータ数が半減できるのに精度は高い。

GoogLeNet / Inception（2014）

GoogLeNet は Inception モジュールとして、複数のネットワークを1つにまとめ、モジュールを積み重ねる、Network In Network の構成がなされている。

Inception モジュールの内部では、異なるフィルタサイズの複数の畳み込み層を同時に通して、結合する処理が行われる。これはパラメータ数を削減して計算コストを下げつつ、複雑なアーキテクチャを組むために行われる。

また、Global Average Pooling も GoogLeNet で採用され、CNN で特徴マップを全結合層へつなぐ際に使用される。

これまでは特徴マップの各画素を順番に切り取って並べることでベクトル化していたのに対し、GAP では一つの特徴マップから Average Pooling で 1×1 のサイズにしたものを並べてベクトル化する。

ResNet（2015）

ResNet は Residual モジュールを採用している。
層を深くすることで精度が向上する一方、深くしすぎると逆伝播時に勾配消失してしまう問題があり、その対策として Residual モジュールが提案された。
Residualモジュールに、1×1 畳み込みを加えて、パラメータを削減しより効率的に学習を行える Bottleneck モジュールも提案された。

He 初期化は、活性化関数に ReLU を用いる際の、最適な重みの初期値として、平均 0 標準偏差 1 の正規分布からランダムに設定する。

Batch Normalization も ResNet で提案された。
ResNet は最大 152 層まで深くしたアーキテクチャが提案されており、各バリエーションの構成は以下の通り。

  • ResNet18
  • ResNet34
  • ResNet50
  • ResNet101
  • ResNet152

MobileNet (2017)

MobileNet はモデルサイズの軽量化を図りながら、高精度の予測を可能としたモデルであり、物体検出などの速度が求められる問題設定のバックボーンとして使用されている。

Depthwise Separable Convolution は、畳み込みの計算を Depthwise Convolution と Pointwise Convolution に分解することで、通常の畳み込み処理からパラメータ数を大きく削減することに成功した。

Depthwise Convolution は、 3 チャネルの画像に対し、各チャネルごとに 1 枚ずつフィルタを用意し、チャネル単位で畳み込みを行うが、各チャネルでの計算結果を足し合わせる処理は行わない。
Depthwise Convolution では入力画像のチャネル数分のチャネルを持った特徴マップが出力される。

TensorFlow では layers.DepthWiseConv2D() で実装できる。
Pointwise Convolution は、1×1 のフィルタをDepthwise Convolution で出力された特徴マップのチャネル数分用意して、畳み込み計算を行う。
TensorFlow では、layers.Conv2D(kernel_size=1, strides=1) で実装できる。

ソースコード

import tensorflow as tf
from tensorflow.keras import models, layers
import numpy as np
# FunctionalAPI によるモデル構築
inputs = layers.Input(shape=(784, ))
x = layers.Dense(64, activation='relu')(inputs)
outputs = layers.Dense(64, activation='relu')(x)
model = models.Model(inputs=inputs, outputs=outputs)
print(model.summary())
# VGGNet
def vgg(input_shape, n_classes):
input = layers.Input(shape=input_shape)
x = layers.Conv2D(64, 3, padding='same', activation='relu')(input)
x = layers.Conv2D(64, 3, padding='same', activation='relu')(x)
x = layers.MaxPool2D(2, strides=2, padding='same')(x)
x = layers.Conv2D(128, 3, padding='same', activation='relu')(x)
x = layers.Conv2D(128, 3, padding='same', activation='relu')(x)
x = layers.MaxPool2D(2, strides=2, padding='same')(x)
x = layers.Conv2D(256, 3, padding='same', activation='relu')(x)
x = layers.Conv2D(256, 3, padding='same', activation='relu')(x)
x = layers.Conv2D(256, 3, padding='same', activation='relu')(x)
x = layers.MaxPool2D(2, strides=2, padding='same')(x)
x = layers.Conv2D(512, 3, padding='same', activation='relu')(x)
x = layers.Conv2D(512, 3, padding='same', activation='relu')(x)
x = layers.Conv2D(512, 3, padding='same', activation='relu')(x)
x = layers.MaxPool2D(2, strides=2, padding='same')(x)
x = layers.Conv2D(512, 3, padding='same', activation='relu')(x)
x = layers.Conv2D(512, 3, padding='same', activation='relu')(x)
x = layers.Conv2D(512, 3, padding='same', activation='relu')(x)
x = layers.MaxPool2D(2, strides=2, padding='same')(x)
x = layers.Flatten()(x)
x = layers.Dense(4096, activation='relu')(x)
x = layers.Dense(4096, activation='relu')(x)
output = layers.Dense(n_classes, activation='softmax')(x)
model = models.Model(input, output)
return model
model = vgg((224, 224, 3), 1000)
print(model.summary())
# GoogLeNet / Inception
def googlenet(input_shape, n_classes):
def inception_block(x, f):
t1 = layers.Conv2D(f[0], 1, activation='relu')(x)
t2 = layers.Conv2D(f[1], 1, activation='relu')(x)
t2 = layers.Conv2D(f[2], 3, padding='same', activation='relu')(t2)
t3 = layers.Conv2D(f[3], 1, activation='relu')(x)
t3 = layers.Conv2D(f[4], 5, padding='same', activation='relu')(t3)
t4 = layers.MaxPool2D(3, 1, padding='same')(x)
t4 = layers.Conv2D(f[5], 1, activation='relu')(t4)
out = layers.concatenate([t1, t2, t3, t4])
return out
input = layers.Input(input_shape)
x = layers.Conv2D(64, 7, strides=2, padding='same', activation='relu')(input)
x = layers.MaxPool2D(3, strides=2, padding='same')(x)
x = layers.Conv2D(64, 1, activation='relu')(x)
x = layers.Conv2D(192, 3, padding='same', activation='relu')(x)
x = layers.MaxPool2D(3, strides=2)(x)
x = inception_block(x, [64, 96, 128, 16, 32, 32])
x = inception_block(x, [128, 128, 192, 32, 96, 64])
x = layers.MaxPool2D(3, strides=2, padding='same')(x)
x = inception_block(x, [192, 96, 208, 16, 48, 64])
x = inception_block(x, [160, 112, 224, 24, 64, 64])
x = inception_block(x, [128, 128, 256, 24, 64, 64])
x = inception_block(x, [112, 144, 288, 32, 64, 64])
x = inception_block(x, [256, 160, 320, 32, 128, 128])
x = layers.MaxPool2D(3, strides=2, padding='same')(x)
x = inception_block(x, [256, 160, 320, 32, 128, 128])
x = inception_block(x, [384, 192, 384, 48, 128, 128])
x = layers.GlobalAveragePooling2D()(x)
x = layers.Dropout(0.4)(x)
output = layers.Dense(n_classes, activation='softmax')(x)
model = models.Model(input, output)
return model
model = googlenet((224, 224, 3), 1000)
print(model.summary())
# ResNet
def resnet(input_shape, n_classes):
def conv_bn_rl(x, f, k=1, s=1, p='same'):
x = layers.Conv2D(f, k, strides=s, padding=p)(x)
x = layers.BatchNormalization()(x)
x = layers.ReLU()(x)
return x
def identity_block(tensor, f):
x = conv_bn_rl(tensor, f)
x = conv_bn_rl(x, f, 3)
x = layers.Conv2D(4*f, 1)(x)
x = layers.BatchNormalization()(x)
x = layers.add([x, tensor])
output = layers.ReLU()(x)
return output
def conv_block(tensor, f, s):
x = conv_bn_rl(tensor, f)
x = conv_bn_rl(x, f, 3, s)
x = layers.Conv2D(4*f, 1)(x)
x = layers.BatchNormalization()(x)
shortcut = layers.Conv2D(4*f, 1, strides=s)(tensor)
shortcut = layers.BatchNormalization()(shortcut)
x = layers.add([x, shortcut])
output = layers.ReLU()(x)
return output
def resnet_block(x, f, r, s=2):
x = conv_block(x, f, s)
for _ in range(r-1):
x = identity_block(x, f)
return x
input = layers.Input(input_shape)
x = conv_bn_rl(input, 64, 7, 2)
x = layers.MaxPool2D(3, strides=2, padding='same')(x)
x = resnet_block(x, 64, 3, 1)
x = resnet_block(x, 128, 4)
x = resnet_block(x, 256, 6)
x = resnet_block(x, 512, 3)
x = layers.GlobalAveragePooling2D()(x)
output = layers.Dense(n_classes, activation='softmax')(x)
model = models.Model(input, output)
return model
model = resnet((224, 224, 3), 1000)
print(model.summary())
# MobileNet
def mobilenet(input_shape, n_classes):
def mobilenet_block(x, f, strides=1):
x = layers.DepthwiseConv2D(3, strides=strides, padding='same')(x)
x = layers.BatchNormalization()(x)
x = layers.ReLU()(x)
x = layers.Conv2D(f, 1, strides=1, padding='same')(x)
x = layers.BatchNormalization()(x)
x = layers.ReLU()(x)
return x
input = layers.Input(input_shape)
x = layers.Conv2D(32, 3, strides=2, padding='same')(input)
x = layers.BatchNormalization()(x)
x = layers.ReLU()(x)
x = mobilenet_block(x, 64)
x = mobilenet_block(x, 128, 2)
x = mobilenet_block(x, 128)
x = mobilenet_block(x, 256, 2)
x = mobilenet_block(x, 256)
x = mobilenet_block(x, 512, 2)
for _ in range(5):
x = mobilenet_block(x, 512)
x = mobilenet_block(x, 1024, 2)
x = mobilenet_block(x, 1024)
x = layers.GlobalAveragePooling2D()(x)
output = layers.Dense(n_classes, activation='softmax')(x)
model = models.Model(input, output)
return model
model = mobilenet((224, 224, 3), 1000)
print(model.summary())
view raw functional_api.py hosted with ❤ by GitHub


実行ログ

C:\Development\Python\Project001\venv\Scripts\python.exe C:/Development/Python/Project001/venv/functional_api.py
2020-07-26 13:38:20.755860: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2
Model: "model"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_1 (InputLayer) [(None, 784)] 0
_________________________________________________________________
dense (Dense) (None, 64) 50240
_________________________________________________________________
dense_1 (Dense) (None, 64) 4160
=================================================================
Total params: 54,400
Trainable params: 54,400
Non-trainable params: 0
_________________________________________________________________
None
2020-07-26 13:38:21.220428: W tensorflow/core/framework/cpu_allocator_impl.cc:81] Allocation of 411041792 exceeds 10% of system memory.
2020-07-26 13:38:21.906974: W tensorflow/core/framework/cpu_allocator_impl.cc:81] Allocation of 411041792 exceeds 10% of system memory.
2020-07-26 13:38:22.030097: W tensorflow/core/framework/cpu_allocator_impl.cc:81] Allocation of 411041792 exceeds 10% of system memory.
Model: "model_1"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_2 (InputLayer) [(None, 224, 224, 3)] 0
_________________________________________________________________
conv2d (Conv2D) (None, 224, 224, 64) 1792
_________________________________________________________________
conv2d_1 (Conv2D) (None, 224, 224, 64) 36928
_________________________________________________________________
max_pooling2d (MaxPooling2D) (None, 112, 112, 64) 0
_________________________________________________________________
conv2d_2 (Conv2D) (None, 112, 112, 128) 73856
_________________________________________________________________
conv2d_3 (Conv2D) (None, 112, 112, 128) 147584
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 56, 56, 128) 0
_________________________________________________________________
conv2d_4 (Conv2D) (None, 56, 56, 256) 295168
_________________________________________________________________
conv2d_5 (Conv2D) (None, 56, 56, 256) 590080
_________________________________________________________________
conv2d_6 (Conv2D) (None, 56, 56, 256) 590080
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 28, 28, 256) 0
_________________________________________________________________
conv2d_7 (Conv2D) (None, 28, 28, 512) 1180160
_________________________________________________________________
conv2d_8 (Conv2D) (None, 28, 28, 512) 2359808
_________________________________________________________________
conv2d_9 (Conv2D) (None, 28, 28, 512) 2359808
_________________________________________________________________
max_pooling2d_3 (MaxPooling2 (None, 14, 14, 512) 0
_________________________________________________________________
conv2d_10 (Conv2D) (None, 14, 14, 512) 2359808
_________________________________________________________________
conv2d_11 (Conv2D) (None, 14, 14, 512) 2359808
_________________________________________________________________
conv2d_12 (Conv2D) (None, 14, 14, 512) 2359808
_________________________________________________________________
max_pooling2d_4 (MaxPooling2 (None, 7, 7, 512) 0
_________________________________________________________________
flatten (Flatten) (None, 25088) 0
_________________________________________________________________
dense_2 (Dense) (None, 4096) 102764544
_________________________________________________________________
dense_3 (Dense) (None, 4096) 16781312
_________________________________________________________________
dense_4 (Dense) (None, 1000) 4097000
=================================================================
Total params: 138,357,544
Trainable params: 138,357,544
Non-trainable params: 0
_________________________________________________________________
None
Model: "model_2"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_3 (InputLayer) [(None, 224, 224, 3) 0
__________________________________________________________________________________________________
conv2d_13 (Conv2D) (None, 112, 112, 64) 9472 input_3[0][0]
__________________________________________________________________________________________________
max_pooling2d_5 (MaxPooling2D) (None, 56, 56, 64) 0 conv2d_13[0][0]
__________________________________________________________________________________________________
conv2d_14 (Conv2D) (None, 56, 56, 64) 4160 max_pooling2d_5[0][0]
__________________________________________________________________________________________________
conv2d_15 (Conv2D) (None, 56, 56, 192) 110784 conv2d_14[0][0]
__________________________________________________________________________________________________
max_pooling2d_6 (MaxPooling2D) (None, 27, 27, 192) 0 conv2d_15[0][0]
__________________________________________________________________________________________________
conv2d_17 (Conv2D) (None, 27, 27, 96) 18528 max_pooling2d_6[0][0]
__________________________________________________________________________________________________
conv2d_19 (Conv2D) (None, 27, 27, 16) 3088 max_pooling2d_6[0][0]
__________________________________________________________________________________________________
max_pooling2d_7 (MaxPooling2D) (None, 27, 27, 192) 0 max_pooling2d_6[0][0]
__________________________________________________________________________________________________
conv2d_16 (Conv2D) (None, 27, 27, 64) 12352 max_pooling2d_6[0][0]
__________________________________________________________________________________________________
conv2d_18 (Conv2D) (None, 27, 27, 128) 110720 conv2d_17[0][0]
__________________________________________________________________________________________________
conv2d_20 (Conv2D) (None, 27, 27, 32) 12832 conv2d_19[0][0]
__________________________________________________________________________________________________
conv2d_21 (Conv2D) (None, 27, 27, 32) 6176 max_pooling2d_7[0][0]
__________________________________________________________________________________________________
concatenate (Concatenate) (None, 27, 27, 256) 0 conv2d_16[0][0]
conv2d_18[0][0]
conv2d_20[0][0]
conv2d_21[0][0]
__________________________________________________________________________________________________
conv2d_23 (Conv2D) (None, 27, 27, 128) 32896 concatenate[0][0]
__________________________________________________________________________________________________
conv2d_25 (Conv2D) (None, 27, 27, 32) 8224 concatenate[0][0]
__________________________________________________________________________________________________
max_pooling2d_8 (MaxPooling2D) (None, 27, 27, 256) 0 concatenate[0][0]
__________________________________________________________________________________________________
conv2d_22 (Conv2D) (None, 27, 27, 128) 32896 concatenate[0][0]
__________________________________________________________________________________________________
conv2d_24 (Conv2D) (None, 27, 27, 192) 221376 conv2d_23[0][0]
__________________________________________________________________________________________________
conv2d_26 (Conv2D) (None, 27, 27, 96) 76896 conv2d_25[0][0]
__________________________________________________________________________________________________
conv2d_27 (Conv2D) (None, 27, 27, 64) 16448 max_pooling2d_8[0][0]
__________________________________________________________________________________________________
concatenate_1 (Concatenate) (None, 27, 27, 480) 0 conv2d_22[0][0]
conv2d_24[0][0]
conv2d_26[0][0]
conv2d_27[0][0]
__________________________________________________________________________________________________
max_pooling2d_9 (MaxPooling2D) (None, 14, 14, 480) 0 concatenate_1[0][0]
__________________________________________________________________________________________________
conv2d_29 (Conv2D) (None, 14, 14, 96) 46176 max_pooling2d_9[0][0]
__________________________________________________________________________________________________
conv2d_31 (Conv2D) (None, 14, 14, 16) 7696 max_pooling2d_9[0][0]
__________________________________________________________________________________________________
max_pooling2d_10 (MaxPooling2D) (None, 14, 14, 480) 0 max_pooling2d_9[0][0]
__________________________________________________________________________________________________
conv2d_28 (Conv2D) (None, 14, 14, 192) 92352 max_pooling2d_9[0][0]
__________________________________________________________________________________________________
conv2d_30 (Conv2D) (None, 14, 14, 208) 179920 conv2d_29[0][0]
__________________________________________________________________________________________________
conv2d_32 (Conv2D) (None, 14, 14, 48) 19248 conv2d_31[0][0]
__________________________________________________________________________________________________
conv2d_33 (Conv2D) (None, 14, 14, 64) 30784 max_pooling2d_10[0][0]
__________________________________________________________________________________________________
concatenate_2 (Concatenate) (None, 14, 14, 512) 0 conv2d_28[0][0]
conv2d_30[0][0]
conv2d_32[0][0]
conv2d_33[0][0]
__________________________________________________________________________________________________
conv2d_35 (Conv2D) (None, 14, 14, 112) 57456 concatenate_2[0][0]
__________________________________________________________________________________________________
conv2d_37 (Conv2D) (None, 14, 14, 24) 12312 concatenate_2[0][0]
__________________________________________________________________________________________________
max_pooling2d_11 (MaxPooling2D) (None, 14, 14, 512) 0 concatenate_2[0][0]
__________________________________________________________________________________________________
conv2d_34 (Conv2D) (None, 14, 14, 160) 82080 concatenate_2[0][0]
__________________________________________________________________________________________________
conv2d_36 (Conv2D) (None, 14, 14, 224) 226016 conv2d_35[0][0]
__________________________________________________________________________________________________
conv2d_38 (Conv2D) (None, 14, 14, 64) 38464 conv2d_37[0][0]
__________________________________________________________________________________________________
conv2d_39 (Conv2D) (None, 14, 14, 64) 32832 max_pooling2d_11[0][0]
__________________________________________________________________________________________________
concatenate_3 (Concatenate) (None, 14, 14, 512) 0 conv2d_34[0][0]
conv2d_36[0][0]
conv2d_38[0][0]
conv2d_39[0][0]
__________________________________________________________________________________________________
conv2d_41 (Conv2D) (None, 14, 14, 128) 65664 concatenate_3[0][0]
__________________________________________________________________________________________________
conv2d_43 (Conv2D) (None, 14, 14, 24) 12312 concatenate_3[0][0]
__________________________________________________________________________________________________
max_pooling2d_12 (MaxPooling2D) (None, 14, 14, 512) 0 concatenate_3[0][0]
__________________________________________________________________________________________________
conv2d_40 (Conv2D) (None, 14, 14, 128) 65664 concatenate_3[0][0]
__________________________________________________________________________________________________
conv2d_42 (Conv2D) (None, 14, 14, 256) 295168 conv2d_41[0][0]
__________________________________________________________________________________________________
conv2d_44 (Conv2D) (None, 14, 14, 64) 38464 conv2d_43[0][0]
__________________________________________________________________________________________________
conv2d_45 (Conv2D) (None, 14, 14, 64) 32832 max_pooling2d_12[0][0]
__________________________________________________________________________________________________
concatenate_4 (Concatenate) (None, 14, 14, 512) 0 conv2d_40[0][0]
conv2d_42[0][0]
conv2d_44[0][0]
conv2d_45[0][0]
__________________________________________________________________________________________________
conv2d_47 (Conv2D) (None, 14, 14, 144) 73872 concatenate_4[0][0]
__________________________________________________________________________________________________
conv2d_49 (Conv2D) (None, 14, 14, 32) 16416 concatenate_4[0][0]
__________________________________________________________________________________________________
max_pooling2d_13 (MaxPooling2D) (None, 14, 14, 512) 0 concatenate_4[0][0]
__________________________________________________________________________________________________
conv2d_46 (Conv2D) (None, 14, 14, 112) 57456 concatenate_4[0][0]
__________________________________________________________________________________________________
conv2d_48 (Conv2D) (None, 14, 14, 288) 373536 conv2d_47[0][0]
__________________________________________________________________________________________________
conv2d_50 (Conv2D) (None, 14, 14, 64) 51264 conv2d_49[0][0]
__________________________________________________________________________________________________
conv2d_51 (Conv2D) (None, 14, 14, 64) 32832 max_pooling2d_13[0][0]
__________________________________________________________________________________________________
concatenate_5 (Concatenate) (None, 14, 14, 528) 0 conv2d_46[0][0]
conv2d_48[0][0]
conv2d_50[0][0]
conv2d_51[0][0]
__________________________________________________________________________________________________
conv2d_53 (Conv2D) (None, 14, 14, 160) 84640 concatenate_5[0][0]
__________________________________________________________________________________________________
conv2d_55 (Conv2D) (None, 14, 14, 32) 16928 concatenate_5[0][0]
__________________________________________________________________________________________________
max_pooling2d_14 (MaxPooling2D) (None, 14, 14, 528) 0 concatenate_5[0][0]
__________________________________________________________________________________________________
conv2d_52 (Conv2D) (None, 14, 14, 256) 135424 concatenate_5[0][0]
__________________________________________________________________________________________________
conv2d_54 (Conv2D) (None, 14, 14, 320) 461120 conv2d_53[0][0]
__________________________________________________________________________________________________
conv2d_56 (Conv2D) (None, 14, 14, 128) 102528 conv2d_55[0][0]
__________________________________________________________________________________________________
conv2d_57 (Conv2D) (None, 14, 14, 128) 67712 max_pooling2d_14[0][0]
__________________________________________________________________________________________________
concatenate_6 (Concatenate) (None, 14, 14, 832) 0 conv2d_52[0][0]
conv2d_54[0][0]
conv2d_56[0][0]
conv2d_57[0][0]
__________________________________________________________________________________________________
max_pooling2d_15 (MaxPooling2D) (None, 7, 7, 832) 0 concatenate_6[0][0]
__________________________________________________________________________________________________
conv2d_59 (Conv2D) (None, 7, 7, 160) 133280 max_pooling2d_15[0][0]
__________________________________________________________________________________________________
conv2d_61 (Conv2D) (None, 7, 7, 32) 26656 max_pooling2d_15[0][0]
__________________________________________________________________________________________________
max_pooling2d_16 (MaxPooling2D) (None, 7, 7, 832) 0 max_pooling2d_15[0][0]
__________________________________________________________________________________________________
conv2d_58 (Conv2D) (None, 7, 7, 256) 213248 max_pooling2d_15[0][0]
__________________________________________________________________________________________________
conv2d_60 (Conv2D) (None, 7, 7, 320) 461120 conv2d_59[0][0]
__________________________________________________________________________________________________
conv2d_62 (Conv2D) (None, 7, 7, 128) 102528 conv2d_61[0][0]
__________________________________________________________________________________________________
conv2d_63 (Conv2D) (None, 7, 7, 128) 106624 max_pooling2d_16[0][0]
__________________________________________________________________________________________________
concatenate_7 (Concatenate) (None, 7, 7, 832) 0 conv2d_58[0][0]
conv2d_60[0][0]
conv2d_62[0][0]
conv2d_63[0][0]
__________________________________________________________________________________________________
conv2d_65 (Conv2D) (None, 7, 7, 192) 159936 concatenate_7[0][0]
__________________________________________________________________________________________________
conv2d_67 (Conv2D) (None, 7, 7, 48) 39984 concatenate_7[0][0]
__________________________________________________________________________________________________
max_pooling2d_17 (MaxPooling2D) (None, 7, 7, 832) 0 concatenate_7[0][0]
__________________________________________________________________________________________________
conv2d_64 (Conv2D) (None, 7, 7, 384) 319872 concatenate_7[0][0]
__________________________________________________________________________________________________
conv2d_66 (Conv2D) (None, 7, 7, 384) 663936 conv2d_65[0][0]
__________________________________________________________________________________________________
conv2d_68 (Conv2D) (None, 7, 7, 128) 153728 conv2d_67[0][0]
__________________________________________________________________________________________________
conv2d_69 (Conv2D) (None, 7, 7, 128) 106624 max_pooling2d_17[0][0]
__________________________________________________________________________________________________
concatenate_8 (Concatenate) (None, 7, 7, 1024) 0 conv2d_64[0][0]
conv2d_66[0][0]
conv2d_68[0][0]
conv2d_69[0][0]
__________________________________________________________________________________________________
global_average_pooling2d (Globa (None, 1024) 0 concatenate_8[0][0]
__________________________________________________________________________________________________
dropout (Dropout) (None, 1024) 0 global_average_pooling2d[0][0]
__________________________________________________________________________________________________
dense_5 (Dense) (None, 1000) 1025000 dropout[0][0]
==================================================================================================
Total params: 6,998,552
Trainable params: 6,998,552
Non-trainable params: 0
__________________________________________________________________________________________________
None
Model: "model_3"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_4 (InputLayer) [(None, 224, 224, 3) 0
__________________________________________________________________________________________________
conv2d_70 (Conv2D) (None, 112, 112, 64) 9472 input_4[0][0]
__________________________________________________________________________________________________
batch_normalization (BatchNorma (None, 112, 112, 64) 256 conv2d_70[0][0]
__________________________________________________________________________________________________
re_lu (ReLU) (None, 112, 112, 64) 0 batch_normalization[0][0]
__________________________________________________________________________________________________
max_pooling2d_18 (MaxPooling2D) (None, 56, 56, 64) 0 re_lu[0][0]
__________________________________________________________________________________________________
conv2d_71 (Conv2D) (None, 56, 56, 64) 4160 max_pooling2d_18[0][0]
__________________________________________________________________________________________________
batch_normalization_1 (BatchNor (None, 56, 56, 64) 256 conv2d_71[0][0]
__________________________________________________________________________________________________
re_lu_1 (ReLU) (None, 56, 56, 64) 0 batch_normalization_1[0][0]
__________________________________________________________________________________________________
conv2d_72 (Conv2D) (None, 56, 56, 64) 36928 re_lu_1[0][0]
__________________________________________________________________________________________________
batch_normalization_2 (BatchNor (None, 56, 56, 64) 256 conv2d_72[0][0]
__________________________________________________________________________________________________
re_lu_2 (ReLU) (None, 56, 56, 64) 0 batch_normalization_2[0][0]
__________________________________________________________________________________________________
conv2d_73 (Conv2D) (None, 56, 56, 256) 16640 re_lu_2[0][0]
__________________________________________________________________________________________________
conv2d_74 (Conv2D) (None, 56, 56, 256) 16640 max_pooling2d_18[0][0]
__________________________________________________________________________________________________
batch_normalization_3 (BatchNor (None, 56, 56, 256) 1024 conv2d_73[0][0]
__________________________________________________________________________________________________
batch_normalization_4 (BatchNor (None, 56, 56, 256) 1024 conv2d_74[0][0]
__________________________________________________________________________________________________
add (Add) (None, 56, 56, 256) 0 batch_normalization_3[0][0]
batch_normalization_4[0][0]
__________________________________________________________________________________________________
re_lu_3 (ReLU) (None, 56, 56, 256) 0 add[0][0]
__________________________________________________________________________________________________
conv2d_75 (Conv2D) (None, 56, 56, 64) 16448 re_lu_3[0][0]
__________________________________________________________________________________________________
batch_normalization_5 (BatchNor (None, 56, 56, 64) 256 conv2d_75[0][0]
__________________________________________________________________________________________________
re_lu_4 (ReLU) (None, 56, 56, 64) 0 batch_normalization_5[0][0]
__________________________________________________________________________________________________
conv2d_76 (Conv2D) (None, 56, 56, 64) 36928 re_lu_4[0][0]
__________________________________________________________________________________________________
batch_normalization_6 (BatchNor (None, 56, 56, 64) 256 conv2d_76[0][0]
__________________________________________________________________________________________________
re_lu_5 (ReLU) (None, 56, 56, 64) 0 batch_normalization_6[0][0]
__________________________________________________________________________________________________
conv2d_77 (Conv2D) (None, 56, 56, 256) 16640 re_lu_5[0][0]
__________________________________________________________________________________________________
batch_normalization_7 (BatchNor (None, 56, 56, 256) 1024 conv2d_77[0][0]
__________________________________________________________________________________________________
add_1 (Add) (None, 56, 56, 256) 0 batch_normalization_7[0][0]
re_lu_3[0][0]
__________________________________________________________________________________________________
re_lu_6 (ReLU) (None, 56, 56, 256) 0 add_1[0][0]
__________________________________________________________________________________________________
conv2d_78 (Conv2D) (None, 56, 56, 64) 16448 re_lu_6[0][0]
__________________________________________________________________________________________________
batch_normalization_8 (BatchNor (None, 56, 56, 64) 256 conv2d_78[0][0]
__________________________________________________________________________________________________
re_lu_7 (ReLU) (None, 56, 56, 64) 0 batch_normalization_8[0][0]
__________________________________________________________________________________________________
conv2d_79 (Conv2D) (None, 56, 56, 64) 36928 re_lu_7[0][0]
__________________________________________________________________________________________________
batch_normalization_9 (BatchNor (None, 56, 56, 64) 256 conv2d_79[0][0]
__________________________________________________________________________________________________
re_lu_8 (ReLU) (None, 56, 56, 64) 0 batch_normalization_9[0][0]
__________________________________________________________________________________________________
conv2d_80 (Conv2D) (None, 56, 56, 256) 16640 re_lu_8[0][0]
__________________________________________________________________________________________________
batch_normalization_10 (BatchNo (None, 56, 56, 256) 1024 conv2d_80[0][0]
__________________________________________________________________________________________________
add_2 (Add) (None, 56, 56, 256) 0 batch_normalization_10[0][0]
re_lu_6[0][0]
__________________________________________________________________________________________________
re_lu_9 (ReLU) (None, 56, 56, 256) 0 add_2[0][0]
__________________________________________________________________________________________________
conv2d_81 (Conv2D) (None, 56, 56, 128) 32896 re_lu_9[0][0]
__________________________________________________________________________________________________
batch_normalization_11 (BatchNo (None, 56, 56, 128) 512 conv2d_81[0][0]
__________________________________________________________________________________________________
re_lu_10 (ReLU) (None, 56, 56, 128) 0 batch_normalization_11[0][0]
__________________________________________________________________________________________________
conv2d_82 (Conv2D) (None, 28, 28, 128) 147584 re_lu_10[0][0]
__________________________________________________________________________________________________
batch_normalization_12 (BatchNo (None, 28, 28, 128) 512 conv2d_82[0][0]
__________________________________________________________________________________________________
re_lu_11 (ReLU) (None, 28, 28, 128) 0 batch_normalization_12[0][0]
__________________________________________________________________________________________________
conv2d_83 (Conv2D) (None, 28, 28, 512) 66048 re_lu_11[0][0]
__________________________________________________________________________________________________
conv2d_84 (Conv2D) (None, 28, 28, 512) 131584 re_lu_9[0][0]
__________________________________________________________________________________________________
batch_normalization_13 (BatchNo (None, 28, 28, 512) 2048 conv2d_83[0][0]
__________________________________________________________________________________________________
batch_normalization_14 (BatchNo (None, 28, 28, 512) 2048 conv2d_84[0][0]
__________________________________________________________________________________________________
add_3 (Add) (None, 28, 28, 512) 0 batch_normalization_13[0][0]
batch_normalization_14[0][0]
__________________________________________________________________________________________________
re_lu_12 (ReLU) (None, 28, 28, 512) 0 add_3[0][0]
__________________________________________________________________________________________________
conv2d_85 (Conv2D) (None, 28, 28, 128) 65664 re_lu_12[0][0]
__________________________________________________________________________________________________
batch_normalization_15 (BatchNo (None, 28, 28, 128) 512 conv2d_85[0][0]
__________________________________________________________________________________________________
re_lu_13 (ReLU) (None, 28, 28, 128) 0 batch_normalization_15[0][0]
__________________________________________________________________________________________________
conv2d_86 (Conv2D) (None, 28, 28, 128) 147584 re_lu_13[0][0]
__________________________________________________________________________________________________
batch_normalization_16 (BatchNo (None, 28, 28, 128) 512 conv2d_86[0][0]
__________________________________________________________________________________________________
re_lu_14 (ReLU) (None, 28, 28, 128) 0 batch_normalization_16[0][0]
__________________________________________________________________________________________________
conv2d_87 (Conv2D) (None, 28, 28, 512) 66048 re_lu_14[0][0]
__________________________________________________________________________________________________
batch_normalization_17 (BatchNo (None, 28, 28, 512) 2048 conv2d_87[0][0]
__________________________________________________________________________________________________
add_4 (Add) (None, 28, 28, 512) 0 batch_normalization_17[0][0]
re_lu_12[0][0]
__________________________________________________________________________________________________
re_lu_15 (ReLU) (None, 28, 28, 512) 0 add_4[0][0]
__________________________________________________________________________________________________
conv2d_88 (Conv2D) (None, 28, 28, 128) 65664 re_lu_15[0][0]
__________________________________________________________________________________________________
batch_normalization_18 (BatchNo (None, 28, 28, 128) 512 conv2d_88[0][0]
__________________________________________________________________________________________________
re_lu_16 (ReLU) (None, 28, 28, 128) 0 batch_normalization_18[0][0]
__________________________________________________________________________________________________
conv2d_89 (Conv2D) (None, 28, 28, 128) 147584 re_lu_16[0][0]
__________________________________________________________________________________________________
batch_normalization_19 (BatchNo (None, 28, 28, 128) 512 conv2d_89[0][0]
__________________________________________________________________________________________________
re_lu_17 (ReLU) (None, 28, 28, 128) 0 batch_normalization_19[0][0]
__________________________________________________________________________________________________
conv2d_90 (Conv2D) (None, 28, 28, 512) 66048 re_lu_17[0][0]
__________________________________________________________________________________________________
batch_normalization_20 (BatchNo (None, 28, 28, 512) 2048 conv2d_90[0][0]
__________________________________________________________________________________________________
add_5 (Add) (None, 28, 28, 512) 0 batch_normalization_20[0][0]
re_lu_15[0][0]
__________________________________________________________________________________________________
re_lu_18 (ReLU) (None, 28, 28, 512) 0 add_5[0][0]
__________________________________________________________________________________________________
conv2d_91 (Conv2D) (None, 28, 28, 128) 65664 re_lu_18[0][0]
__________________________________________________________________________________________________
batch_normalization_21 (BatchNo (None, 28, 28, 128) 512 conv2d_91[0][0]
__________________________________________________________________________________________________
re_lu_19 (ReLU) (None, 28, 28, 128) 0 batch_normalization_21[0][0]
__________________________________________________________________________________________________
conv2d_92 (Conv2D) (None, 28, 28, 128) 147584 re_lu_19[0][0]
__________________________________________________________________________________________________
batch_normalization_22 (BatchNo (None, 28, 28, 128) 512 conv2d_92[0][0]
__________________________________________________________________________________________________
re_lu_20 (ReLU) (None, 28, 28, 128) 0 batch_normalization_22[0][0]
__________________________________________________________________________________________________
conv2d_93 (Conv2D) (None, 28, 28, 512) 66048 re_lu_20[0][0]
__________________________________________________________________________________________________
batch_normalization_23 (BatchNo (None, 28, 28, 512) 2048 conv2d_93[0][0]
__________________________________________________________________________________________________
add_6 (Add) (None, 28, 28, 512) 0 batch_normalization_23[0][0]
re_lu_18[0][0]
__________________________________________________________________________________________________
re_lu_21 (ReLU) (None, 28, 28, 512) 0 add_6[0][0]
__________________________________________________________________________________________________
conv2d_94 (Conv2D) (None, 28, 28, 256) 131328 re_lu_21[0][0]
__________________________________________________________________________________________________
batch_normalization_24 (BatchNo (None, 28, 28, 256) 1024 conv2d_94[0][0]
__________________________________________________________________________________________________
re_lu_22 (ReLU) (None, 28, 28, 256) 0 batch_normalization_24[0][0]
__________________________________________________________________________________________________
conv2d_95 (Conv2D) (None, 14, 14, 256) 590080 re_lu_22[0][0]
__________________________________________________________________________________________________
batch_normalization_25 (BatchNo (None, 14, 14, 256) 1024 conv2d_95[0][0]
__________________________________________________________________________________________________
re_lu_23 (ReLU) (None, 14, 14, 256) 0 batch_normalization_25[0][0]
__________________________________________________________________________________________________
conv2d_96 (Conv2D) (None, 14, 14, 1024) 263168 re_lu_23[0][0]
__________________________________________________________________________________________________
conv2d_97 (Conv2D) (None, 14, 14, 1024) 525312 re_lu_21[0][0]
__________________________________________________________________________________________________
batch_normalization_26 (BatchNo (None, 14, 14, 1024) 4096 conv2d_96[0][0]
__________________________________________________________________________________________________
batch_normalization_27 (BatchNo (None, 14, 14, 1024) 4096 conv2d_97[0][0]
__________________________________________________________________________________________________
add_7 (Add) (None, 14, 14, 1024) 0 batch_normalization_26[0][0]
batch_normalization_27[0][0]
__________________________________________________________________________________________________
re_lu_24 (ReLU) (None, 14, 14, 1024) 0 add_7[0][0]
__________________________________________________________________________________________________
conv2d_98 (Conv2D) (None, 14, 14, 256) 262400 re_lu_24[0][0]
__________________________________________________________________________________________________
batch_normalization_28 (BatchNo (None, 14, 14, 256) 1024 conv2d_98[0][0]
__________________________________________________________________________________________________
re_lu_25 (ReLU) (None, 14, 14, 256) 0 batch_normalization_28[0][0]
__________________________________________________________________________________________________
conv2d_99 (Conv2D) (None, 14, 14, 256) 590080 re_lu_25[0][0]
__________________________________________________________________________________________________
batch_normalization_29 (BatchNo (None, 14, 14, 256) 1024 conv2d_99[0][0]
__________________________________________________________________________________________________
re_lu_26 (ReLU) (None, 14, 14, 256) 0 batch_normalization_29[0][0]
__________________________________________________________________________________________________
conv2d_100 (Conv2D) (None, 14, 14, 1024) 263168 re_lu_26[0][0]
__________________________________________________________________________________________________
batch_normalization_30 (BatchNo (None, 14, 14, 1024) 4096 conv2d_100[0][0]
__________________________________________________________________________________________________
add_8 (Add) (None, 14, 14, 1024) 0 batch_normalization_30[0][0]
re_lu_24[0][0]
__________________________________________________________________________________________________
re_lu_27 (ReLU) (None, 14, 14, 1024) 0 add_8[0][0]
__________________________________________________________________________________________________
conv2d_101 (Conv2D) (None, 14, 14, 256) 262400 re_lu_27[0][0]
__________________________________________________________________________________________________
batch_normalization_31 (BatchNo (None, 14, 14, 256) 1024 conv2d_101[0][0]
__________________________________________________________________________________________________
re_lu_28 (ReLU) (None, 14, 14, 256) 0 batch_normalization_31[0][0]
__________________________________________________________________________________________________
conv2d_102 (Conv2D) (None, 14, 14, 256) 590080 re_lu_28[0][0]
__________________________________________________________________________________________________
batch_normalization_32 (BatchNo (None, 14, 14, 256) 1024 conv2d_102[0][0]
__________________________________________________________________________________________________
re_lu_29 (ReLU) (None, 14, 14, 256) 0 batch_normalization_32[0][0]
__________________________________________________________________________________________________
conv2d_103 (Conv2D) (None, 14, 14, 1024) 263168 re_lu_29[0][0]
__________________________________________________________________________________________________
batch_normalization_33 (BatchNo (None, 14, 14, 1024) 4096 conv2d_103[0][0]
__________________________________________________________________________________________________
add_9 (Add) (None, 14, 14, 1024) 0 batch_normalization_33[0][0]
re_lu_27[0][0]
__________________________________________________________________________________________________
re_lu_30 (ReLU) (None, 14, 14, 1024) 0 add_9[0][0]
__________________________________________________________________________________________________
conv2d_104 (Conv2D) (None, 14, 14, 256) 262400 re_lu_30[0][0]
__________________________________________________________________________________________________
batch_normalization_34 (BatchNo (None, 14, 14, 256) 1024 conv2d_104[0][0]
__________________________________________________________________________________________________
re_lu_31 (ReLU) (None, 14, 14, 256) 0 batch_normalization_34[0][0]
__________________________________________________________________________________________________
conv2d_105 (Conv2D) (None, 14, 14, 256) 590080 re_lu_31[0][0]
__________________________________________________________________________________________________
batch_normalization_35 (BatchNo (None, 14, 14, 256) 1024 conv2d_105[0][0]
__________________________________________________________________________________________________
re_lu_32 (ReLU) (None, 14, 14, 256) 0 batch_normalization_35[0][0]
__________________________________________________________________________________________________
conv2d_106 (Conv2D) (None, 14, 14, 1024) 263168 re_lu_32[0][0]
__________________________________________________________________________________________________
batch_normalization_36 (BatchNo (None, 14, 14, 1024) 4096 conv2d_106[0][0]
__________________________________________________________________________________________________
add_10 (Add) (None, 14, 14, 1024) 0 batch_normalization_36[0][0]
re_lu_30[0][0]
__________________________________________________________________________________________________
re_lu_33 (ReLU) (None, 14, 14, 1024) 0 add_10[0][0]
__________________________________________________________________________________________________
conv2d_107 (Conv2D) (None, 14, 14, 256) 262400 re_lu_33[0][0]
__________________________________________________________________________________________________
batch_normalization_37 (BatchNo (None, 14, 14, 256) 1024 conv2d_107[0][0]
__________________________________________________________________________________________________
re_lu_34 (ReLU) (None, 14, 14, 256) 0 batch_normalization_37[0][0]
__________________________________________________________________________________________________
conv2d_108 (Conv2D) (None, 14, 14, 256) 590080 re_lu_34[0][0]
__________________________________________________________________________________________________
batch_normalization_38 (BatchNo (None, 14, 14, 256) 1024 conv2d_108[0][0]
__________________________________________________________________________________________________
re_lu_35 (ReLU) (None, 14, 14, 256) 0 batch_normalization_38[0][0]
__________________________________________________________________________________________________
conv2d_109 (Conv2D) (None, 14, 14, 1024) 263168 re_lu_35[0][0]
__________________________________________________________________________________________________
batch_normalization_39 (BatchNo (None, 14, 14, 1024) 4096 conv2d_109[0][0]
__________________________________________________________________________________________________
add_11 (Add) (None, 14, 14, 1024) 0 batch_normalization_39[0][0]
re_lu_33[0][0]
__________________________________________________________________________________________________
re_lu_36 (ReLU) (None, 14, 14, 1024) 0 add_11[0][0]
__________________________________________________________________________________________________
conv2d_110 (Conv2D) (None, 14, 14, 256) 262400 re_lu_36[0][0]
__________________________________________________________________________________________________
batch_normalization_40 (BatchNo (None, 14, 14, 256) 1024 conv2d_110[0][0]
__________________________________________________________________________________________________
re_lu_37 (ReLU) (None, 14, 14, 256) 0 batch_normalization_40[0][0]
__________________________________________________________________________________________________
conv2d_111 (Conv2D) (None, 14, 14, 256) 590080 re_lu_37[0][0]
__________________________________________________________________________________________________
batch_normalization_41 (BatchNo (None, 14, 14, 256) 1024 conv2d_111[0][0]
__________________________________________________________________________________________________
re_lu_38 (ReLU) (None, 14, 14, 256) 0 batch_normalization_41[0][0]
__________________________________________________________________________________________________
conv2d_112 (Conv2D) (None, 14, 14, 1024) 263168 re_lu_38[0][0]
__________________________________________________________________________________________________
batch_normalization_42 (BatchNo (None, 14, 14, 1024) 4096 conv2d_112[0][0]
__________________________________________________________________________________________________
add_12 (Add) (None, 14, 14, 1024) 0 batch_normalization_42[0][0]
re_lu_36[0][0]
__________________________________________________________________________________________________
re_lu_39 (ReLU) (None, 14, 14, 1024) 0 add_12[0][0]
__________________________________________________________________________________________________
conv2d_113 (Conv2D) (None, 14, 14, 512) 524800 re_lu_39[0][0]
__________________________________________________________________________________________________
batch_normalization_43 (BatchNo (None, 14, 14, 512) 2048 conv2d_113[0][0]
__________________________________________________________________________________________________
re_lu_40 (ReLU) (None, 14, 14, 512) 0 batch_normalization_43[0][0]
__________________________________________________________________________________________________
conv2d_114 (Conv2D) (None, 7, 7, 512) 2359808 re_lu_40[0][0]
__________________________________________________________________________________________________
batch_normalization_44 (BatchNo (None, 7, 7, 512) 2048 conv2d_114[0][0]
__________________________________________________________________________________________________
re_lu_41 (ReLU) (None, 7, 7, 512) 0 batch_normalization_44[0][0]
__________________________________________________________________________________________________
conv2d_115 (Conv2D) (None, 7, 7, 2048) 1050624 re_lu_41[0][0]
__________________________________________________________________________________________________
conv2d_116 (Conv2D) (None, 7, 7, 2048) 2099200 re_lu_39[0][0]
__________________________________________________________________________________________________
batch_normalization_45 (BatchNo (None, 7, 7, 2048) 8192 conv2d_115[0][0]
__________________________________________________________________________________________________
batch_normalization_46 (BatchNo (None, 7, 7, 2048) 8192 conv2d_116[0][0]
__________________________________________________________________________________________________
add_13 (Add) (None, 7, 7, 2048) 0 batch_normalization_45[0][0]
batch_normalization_46[0][0]
__________________________________________________________________________________________________
re_lu_42 (ReLU) (None, 7, 7, 2048) 0 add_13[0][0]
__________________________________________________________________________________________________
conv2d_117 (Conv2D) (None, 7, 7, 512) 1049088 re_lu_42[0][0]
__________________________________________________________________________________________________
batch_normalization_47 (BatchNo (None, 7, 7, 512) 2048 conv2d_117[0][0]
__________________________________________________________________________________________________
re_lu_43 (ReLU) (None, 7, 7, 512) 0 batch_normalization_47[0][0]
__________________________________________________________________________________________________
conv2d_118 (Conv2D) (None, 7, 7, 512) 2359808 re_lu_43[0][0]
__________________________________________________________________________________________________
batch_normalization_48 (BatchNo (None, 7, 7, 512) 2048 conv2d_118[0][0]
__________________________________________________________________________________________________
re_lu_44 (ReLU) (None, 7, 7, 512) 0 batch_normalization_48[0][0]
__________________________________________________________________________________________________
conv2d_119 (Conv2D) (None, 7, 7, 2048) 1050624 re_lu_44[0][0]
__________________________________________________________________________________________________
batch_normalization_49 (BatchNo (None, 7, 7, 2048) 8192 conv2d_119[0][0]
__________________________________________________________________________________________________
add_14 (Add) (None, 7, 7, 2048) 0 batch_normalization_49[0][0]
re_lu_42[0][0]
__________________________________________________________________________________________________
re_lu_45 (ReLU) (None, 7, 7, 2048) 0 add_14[0][0]
__________________________________________________________________________________________________
conv2d_120 (Conv2D) (None, 7, 7, 512) 1049088 re_lu_45[0][0]
__________________________________________________________________________________________________
batch_normalization_50 (BatchNo (None, 7, 7, 512) 2048 conv2d_120[0][0]
__________________________________________________________________________________________________
re_lu_46 (ReLU) (None, 7, 7, 512) 0 batch_normalization_50[0][0]
__________________________________________________________________________________________________
conv2d_121 (Conv2D) (None, 7, 7, 512) 2359808 re_lu_46[0][0]
__________________________________________________________________________________________________
batch_normalization_51 (BatchNo (None, 7, 7, 512) 2048 conv2d_121[0][0]
__________________________________________________________________________________________________
re_lu_47 (ReLU) (None, 7, 7, 512) 0 batch_normalization_51[0][0]
__________________________________________________________________________________________________
conv2d_122 (Conv2D) (None, 7, 7, 2048) 1050624 re_lu_47[0][0]
__________________________________________________________________________________________________
batch_normalization_52 (BatchNo (None, 7, 7, 2048) 8192 conv2d_122[0][0]
__________________________________________________________________________________________________
add_15 (Add) (None, 7, 7, 2048) 0 batch_normalization_52[0][0]
re_lu_45[0][0]
__________________________________________________________________________________________________
re_lu_48 (ReLU) (None, 7, 7, 2048) 0 add_15[0][0]
__________________________________________________________________________________________________
global_average_pooling2d_1 (Glo (None, 2048) 0 re_lu_48[0][0]
__________________________________________________________________________________________________
dense_6 (Dense) (None, 1000) 2049000 global_average_pooling2d_1[0][0]
==================================================================================================
Total params: 25,636,712
Trainable params: 25,583,592
Non-trainable params: 53,120
__________________________________________________________________________________________________
None
Model: "model_4"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_5 (InputLayer) [(None, 224, 224, 3)] 0
_________________________________________________________________
conv2d_123 (Conv2D) (None, 112, 112, 32) 896
_________________________________________________________________
batch_normalization_53 (Batc (None, 112, 112, 32) 128
_________________________________________________________________
re_lu_49 (ReLU) (None, 112, 112, 32) 0
_________________________________________________________________
depthwise_conv2d (DepthwiseC (None, 112, 112, 32) 320
_________________________________________________________________
batch_normalization_54 (Batc (None, 112, 112, 32) 128
_________________________________________________________________
re_lu_50 (ReLU) (None, 112, 112, 32) 0
_________________________________________________________________
conv2d_124 (Conv2D) (None, 112, 112, 64) 2112
_________________________________________________________________
batch_normalization_55 (Batc (None, 112, 112, 64) 256
_________________________________________________________________
re_lu_51 (ReLU) (None, 112, 112, 64) 0
_________________________________________________________________
depthwise_conv2d_1 (Depthwis (None, 56, 56, 64) 640
_________________________________________________________________
batch_normalization_56 (Batc (None, 56, 56, 64) 256
_________________________________________________________________
re_lu_52 (ReLU) (None, 56, 56, 64) 0
_________________________________________________________________
conv2d_125 (Conv2D) (None, 56, 56, 128) 8320
_________________________________________________________________
batch_normalization_57 (Batc (None, 56, 56, 128) 512
_________________________________________________________________
re_lu_53 (ReLU) (None, 56, 56, 128) 0
_________________________________________________________________
depthwise_conv2d_2 (Depthwis (None, 56, 56, 128) 1280
_________________________________________________________________
batch_normalization_58 (Batc (None, 56, 56, 128) 512
_________________________________________________________________
re_lu_54 (ReLU) (None, 56, 56, 128) 0
_________________________________________________________________
conv2d_126 (Conv2D) (None, 56, 56, 128) 16512
_________________________________________________________________
batch_normalization_59 (Batc (None, 56, 56, 128) 512
_________________________________________________________________
re_lu_55 (ReLU) (None, 56, 56, 128) 0
_________________________________________________________________
depthwise_conv2d_3 (Depthwis (None, 28, 28, 128) 1280
_________________________________________________________________
batch_normalization_60 (Batc (None, 28, 28, 128) 512
_________________________________________________________________
re_lu_56 (ReLU) (None, 28, 28, 128) 0
_________________________________________________________________
conv2d_127 (Conv2D) (None, 28, 28, 256) 33024
_________________________________________________________________
batch_normalization_61 (Batc (None, 28, 28, 256) 1024
_________________________________________________________________
re_lu_57 (ReLU) (None, 28, 28, 256) 0
_________________________________________________________________
depthwise_conv2d_4 (Depthwis (None, 28, 28, 256) 2560
_________________________________________________________________
batch_normalization_62 (Batc (None, 28, 28, 256) 1024
_________________________________________________________________
re_lu_58 (ReLU) (None, 28, 28, 256) 0
_________________________________________________________________
conv2d_128 (Conv2D) (None, 28, 28, 256) 65792
_________________________________________________________________
batch_normalization_63 (Batc (None, 28, 28, 256) 1024
_________________________________________________________________
re_lu_59 (ReLU) (None, 28, 28, 256) 0
_________________________________________________________________
depthwise_conv2d_5 (Depthwis (None, 14, 14, 256) 2560
_________________________________________________________________
batch_normalization_64 (Batc (None, 14, 14, 256) 1024
_________________________________________________________________
re_lu_60 (ReLU) (None, 14, 14, 256) 0
_________________________________________________________________
conv2d_129 (Conv2D) (None, 14, 14, 512) 131584
_________________________________________________________________
batch_normalization_65 (Batc (None, 14, 14, 512) 2048
_________________________________________________________________
re_lu_61 (ReLU) (None, 14, 14, 512) 0
_________________________________________________________________
depthwise_conv2d_6 (Depthwis (None, 14, 14, 512) 5120
_________________________________________________________________
batch_normalization_66 (Batc (None, 14, 14, 512) 2048
_________________________________________________________________
re_lu_62 (ReLU) (None, 14, 14, 512) 0
_________________________________________________________________
conv2d_130 (Conv2D) (None, 14, 14, 512) 262656
_________________________________________________________________
batch_normalization_67 (Batc (None, 14, 14, 512) 2048
_________________________________________________________________
re_lu_63 (ReLU) (None, 14, 14, 512) 0
_________________________________________________________________
depthwise_conv2d_7 (Depthwis (None, 14, 14, 512) 5120
_________________________________________________________________
batch_normalization_68 (Batc (None, 14, 14, 512) 2048
_________________________________________________________________
re_lu_64 (ReLU) (None, 14, 14, 512) 0
_________________________________________________________________
conv2d_131 (Conv2D) (None, 14, 14, 512) 262656
_________________________________________________________________
batch_normalization_69 (Batc (None, 14, 14, 512) 2048
_________________________________________________________________
re_lu_65 (ReLU) (None, 14, 14, 512) 0
_________________________________________________________________
depthwise_conv2d_8 (Depthwis (None, 14, 14, 512) 5120
_________________________________________________________________
batch_normalization_70 (Batc (None, 14, 14, 512) 2048
_________________________________________________________________
re_lu_66 (ReLU) (None, 14, 14, 512) 0
_________________________________________________________________
conv2d_132 (Conv2D) (None, 14, 14, 512) 262656
_________________________________________________________________
batch_normalization_71 (Batc (None, 14, 14, 512) 2048
_________________________________________________________________
re_lu_67 (ReLU) (None, 14, 14, 512) 0
_________________________________________________________________
depthwise_conv2d_9 (Depthwis (None, 14, 14, 512) 5120
_________________________________________________________________
batch_normalization_72 (Batc (None, 14, 14, 512) 2048
_________________________________________________________________
re_lu_68 (ReLU) (None, 14, 14, 512) 0
_________________________________________________________________
conv2d_133 (Conv2D) (None, 14, 14, 512) 262656
_________________________________________________________________
batch_normalization_73 (Batc (None, 14, 14, 512) 2048
_________________________________________________________________
re_lu_69 (ReLU) (None, 14, 14, 512) 0
_________________________________________________________________
depthwise_conv2d_10 (Depthwi (None, 14, 14, 512) 5120
_________________________________________________________________
batch_normalization_74 (Batc (None, 14, 14, 512) 2048
_________________________________________________________________
re_lu_70 (ReLU) (None, 14, 14, 512) 0
_________________________________________________________________
conv2d_134 (Conv2D) (None, 14, 14, 512) 262656
_________________________________________________________________
batch_normalization_75 (Batc (None, 14, 14, 512) 2048
_________________________________________________________________
re_lu_71 (ReLU) (None, 14, 14, 512) 0
_________________________________________________________________
depthwise_conv2d_11 (Depthwi (None, 7, 7, 512) 5120
_________________________________________________________________
batch_normalization_76 (Batc (None, 7, 7, 512) 2048
_________________________________________________________________
re_lu_72 (ReLU) (None, 7, 7, 512) 0
_________________________________________________________________
conv2d_135 (Conv2D) (None, 7, 7, 1024) 525312
_________________________________________________________________
batch_normalization_77 (Batc (None, 7, 7, 1024) 4096
_________________________________________________________________
re_lu_73 (ReLU) (None, 7, 7, 1024) 0
_________________________________________________________________
depthwise_conv2d_12 (Depthwi (None, 7, 7, 1024) 10240
_________________________________________________________________
batch_normalization_78 (Batc (None, 7, 7, 1024) 4096
_________________________________________________________________
re_lu_74 (ReLU) (None, 7, 7, 1024) 0
_________________________________________________________________
conv2d_136 (Conv2D) (None, 7, 7, 1024) 1049600
_________________________________________________________________
batch_normalization_79 (Batc (None, 7, 7, 1024) 4096
_________________________________________________________________
re_lu_75 (ReLU) (None, 7, 7, 1024) 0
_________________________________________________________________
global_average_pooling2d_2 ( (None, 1024) 0
_________________________________________________________________
dense_7 (Dense) (None, 1000) 1025000
=================================================================
Total params: 4,264,808
Trainable params: 4,242,920
Non-trainable params: 21,888
_________________________________________________________________
None
Process finished with exit code 0
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Posted in  on 7/26/2020 by rteak |