ResNet50 vs InceptionV3 vs Xception vs NASNet - Introduction to Transfer Learning
Posted June 28, 2019 by Gowri Shankar ‐ 22 min read
Transfer learning is an ML methodology that enables to reuse a model developed for one task to another task. The applications are predominantly in Deep Learning for computer vision and natural language processing.
Objective:
Objective of this kernel is to introduce transfer learning to beginners. I have taken the following deep neural network applications
- ResNet50
- InceptionV3
- Xception
- NASNet
Accuracy versus Computational Demand (Left) and Number of Parameters (Right)
Transfer Learning
Transfer learning is an ML methodology that enables to reuse a model developed for one task to another task. The applications are predominantly in Deep Learning for computer vision and natural language processing. This kernel introduces one on how to use Keras transfer learning applications.
ResNet50 (APTOS Accuracy: 0.396)
Created By: Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
Literature: Deep Residual Learning for Image Recognition
Topological Depth: 152 Layers
ImageNet Validation Accuracy: Top-1 Accuracy: 0.749 Top-5 Accuracy: 0.921
InceptionV3 (APTOS Accuracy: 0.559)
Created By: Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jonathon Shlens, Zbigniew Wojna
Literature: Rethinking the Inception Architecture for Computer Vision
Topological Depth: 159 Layers
ImageNet Validation Accuracy: Top-1 Accuracy: 0.779 Top-5 Accuracy: 0.937
Xception (APTOS Accuracy: 0.509)
Created By: François Chollet
Literature: Xception: Deep Learning with Depthwise Separable Convolutions
Topological Depth: 126 Layers
ImageNet Validation Accuracy: Top-1 Accuracy: 0.790 Top-5 Accuracy: 0.945
NASNet (APTOS Accuracy: TBD)
Created By: Barret Zoph, Vijay Vasudevan, Jonathon Shlens, Quoc V. Le
Literature: Learning Transferable Architectures for Scalable Image Recognition
Topological Depth: ~1040
ImageNet Validation Accuracy: Top-1 Accuracy: 0.825 Top-5 Accuracy: 0.960
import os
print(os.listdir("../input"))
['aptos2019-blindness-detection', 'keras-pretrained-models', 'nasnetlarge']
Preprocess
# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load in
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
# Input data files are available in the "../input/" directory.
# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory
train_df = pd.read_csv("../input/aptos2019-blindness-detection/train.csv")
print("Shape of train data: {0}".format(train_df.shape))
test_df = pd.read_csv("../input/aptos2019-blindness-detection/test.csv")
print("Shape of test data: {0}".format(test_df.shape))
diagnosis_df = pd.DataFrame({
'diagnosis': [0, 1, 2, 3, 4],
'diagnosis_label': ['No DR', 'Mild', 'Moderate', 'Severe', 'Proliferative DR']
})
train_df = train_df.merge(diagnosis_df, how="left", on="diagnosis")
train_image_files = [os.path.join(dp, f) for dp, dn, fn in os.walk(os.path.expanduser("../input/aptos2019-blindness-detection/train_images")) for f in fn]
train_images_df = pd.DataFrame({
'files': train_image_files,
'id_code': [file.split('/')[4].split('.')[0] for file in train_image_files],
})
train_df = train_df.merge(train_images_df, how="left", on="id_code")
del train_images_df
print("Shape of train data: {0}".format(train_df.shape))
test_image_files = [os.path.join(dp, f) for dp, dn, fn in os.walk(os.path.expanduser("../input/aptos2019-blindness-detection/test_images")) for f in fn]
test_images_df = pd.DataFrame({
'files': test_image_files,
'id_code': [file.split('/')[4].split('.')[0] for file in test_image_files],
})
test_df = test_df.merge(test_images_df, how="left", on="id_code")
del test_images_df
print("Shape of test data: {0}".format(test_df.shape))
# Any results you write to the current directory are saved as output.
Shape of train data: (3662, 2)
Shape of test data: (1928, 1)
Shape of train data: (3662, 4)
Shape of test data: (1928, 2)
train_df.head()
| id_code | diagnosis | diagnosis_label | files | |
|---|---|---|---|---|
| 0 | 000c1434d8d7 | 2 | Moderate | ../input/aptos2019-blindness-detection/train_i... |
| 1 | 001639a390f0 | 4 | Proliferative DR | ../input/aptos2019-blindness-detection/train_i... |
| 2 | 0024cdab0c1e | 1 | Mild | ../input/aptos2019-blindness-detection/train_i... |
| 3 | 002c21358ce6 | 0 | No DR | ../input/aptos2019-blindness-detection/train_i... |
| 4 | 005b95c28852 | 0 | No DR | ../input/aptos2019-blindness-detection/train_i... |
test_df.head()
| id_code | files | |
|---|---|---|
| 0 | 0005cfc8afb6 | ../input/aptos2019-blindness-detection/test_im... |
| 1 | 003f0afdcd15 | ../input/aptos2019-blindness-detection/test_im... |
| 2 | 006efc72b638 | ../input/aptos2019-blindness-detection/test_im... |
| 3 | 00836aaacf06 | ../input/aptos2019-blindness-detection/test_im... |
| 4 | 009245722fa4 | ../input/aptos2019-blindness-detection/test_im... |
IMG_SIZE = 150
N_CLASSES = train_df.diagnosis.nunique()
CLASSES = list(map(str, range(N_CLASSES)))
BATCH_SIZE = 32
EPOCH_STEPS = 10
EPOCHS = 25
NB_FILTERS = 32
KERNEL_SIZE = 4
CHANNELS = 3
Data Generator: Train, Validation and Test Datasets
import tensorflow as tf
print(tf.__version__)
from keras.preprocessing.image import ImageDataGenerator
train_df["diagnosis"] = train_df["diagnosis"].astype(str)
train_data_gen = ImageDataGenerator(
# featurewise_center = True, # Set input mean to 0 over the dataset
samplewise_center = True, # set each sample mean to 0
featurewise_std_normalization = True, # Divide inputs by std of the dataset
samplewise_std_normalization = True, # Divide each input by its std
# zca_whitening = True, # Apply ZCA whitening
zca_epsilon = 1e-06, # Epsilon for ZCA whitening,
rotation_range = 30, # randomly rotate imges in the range (degrees, 0 to 189)
width_shift_range = 0.1, # randomly shift images horizontally (fraction of total width)
height_shift_range = 0.1, # Randomly shift images vertically (fraction of total height)
shear_range = 0, # set range for random shear
zoom_range = [0.75, 1.25], # set range for random zoom
channel_shift_range = 0.05, # set range for random channel shifts
fill_mode = 'constant', # set mode for filling points outside the input boundaries
cval = 0, # value used for fill_mode
horizontal_flip = True,
vertical_flip = True,
rescale = 1/255.,
preprocessing_function = None,
validation_split=0.1
)
train_data = train_data_gen.flow_from_dataframe(
dataframe=train_df,
x_col="files",
y_col="diagnosis",
batch_size=BATCH_SIZE,
shuffle=True,
classes=CLASSES,
class_mode="categorical",
target_size=(IMG_SIZE, IMG_SIZE),
subset="training"
)
validation_data = train_data_gen.flow_from_dataframe(
dataframe=train_df,
x_col="files",
y_col="diagnosis",
batch_size=BATCH_SIZE,
shuffle=True,
classes=CLASSES,
class_mode="categorical",
target_size=(IMG_SIZE, IMG_SIZE),
subset="validation"
)
test_data_gen = ImageDataGenerator(rescale=1./255)
test_data = test_data_gen.flow_from_dataframe(
dataframe=test_df,
x_col="files",
target_size=(IMG_SIZE, IMG_SIZE),
batch_size = 1,
shuffle=False,
class_mode=None
)
Transfer Learning Assets
from tensorflow.python.keras.applications import ResNet50, InceptionV3, Xception, NASNetLarge
print(os.listdir(("../input/keras-pretrained-models/")))
print(os.listdir(("../input/nasnetlarge/")))
model_resnet50 = ResNet50(
weights="../input/keras-pretrained-models/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5",
include_top=False,
input_tensor=tf.keras.layers.Input(shape=(IMG_SIZE, IMG_SIZE, 3))
)
model_inception_v3 = InceptionV3(
weights="../input/keras-pretrained-models/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5",
include_top=False,
input_tensor=tf.keras.layers.Input(shape=(IMG_SIZE, IMG_SIZE, 3))
)
model_xception = Xception(
weights="../input/keras-pretrained-models/xception_weights_tf_dim_ordering_tf_kernels_notop.h5",
include_top=False,
input_tensor=tf.keras.layers.Input(shape=(IMG_SIZE, IMG_SIZE, 3))
)
model_nasnet_large = NASNetLarge(
weights="../input/nasnetlarge/NASNet-large-no-top.h5",
include_top=False,
input_tensor=tf.keras.layers.Input(shape=(IMG_SIZE, IMG_SIZE, 3))
)
['inception_v3_weights_tf_dim_ordering_tf_kernels.h5', 'resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5', 'imagenet_class_index.json', 'xception_weights_tf_dim_ordering_tf_kernels_notop.h5', 'inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5', 'inception_resnet_v2_weights_tf_dim_ordering_tf_kernels.h5', 'xception_weights_tf_dim_ordering_tf_kernels.h5', 'inception_resnet_v2_weights_tf_dim_ordering_tf_kernels_notop.h5', 'resnet50_weights_tf_dim_ordering_tf_kernels.h5', 'vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5', 'Kuszma.JPG']
['NASNet-large-no-top.h5']
/opt/conda/lib/python3.6/site-packages/keras_applications/resnet50.py:265: UserWarning: The output shape of `ResNet50(include_top=False)` has been changed since Keras 2.2.0.
warnings.warn('The output shape of `ResNet50(include_top=False)` '
def create_model():
model = tf.keras.models.Sequential([
tf.keras.layers.Conv2D(NB_FILTERS, (KERNEL_SIZE, KERNEL_SIZE), padding="valid", strides=1, input_shape=(IMG_SIZE, IMG_SIZE, CHANNELS), activation="relu"),
tf.keras.layers.Conv2D(NB_FILTERS, (KERNEL_SIZE, KERNEL_SIZE), activation="relu"),
tf.keras.layers.Conv2D(NB_FILTERS, (KERNEL_SIZE, KERNEL_SIZE), activation="relu"),
tf.keras.layers.MaxPooling2D(pool_size=(8, 8)),
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(2048, activation="relu"),
tf.keras.layers.Dropout(0.25),
tf.keras.layers.Dense(2048, activation="relu"),
tf.keras.layers.Dropout(0.25),
tf.keras.layers.Dense(N_CLASSES, activation="softmax")
])
return model
# Resnet50: 0.396
# create_model: 0.152
# InceptionV3: 0.559
# Xception: 0.509
def get_model(model):
X = model.output
X = tf.keras.layers.GlobalAveragePooling2D()(X)
X = tf.keras.layers.Dense(2048, activation='relu')(X)
X = tf.keras.layers.Dropout(0.25)(X)
X = tf.keras.layers.Dense(1024, activation='relu')(X)
X = tf.keras.layers.Dropout(0.25)(X)
X = tf.keras.layers.Dense(512, activation='relu')(X)
X = tf.keras.layers.Dropout(0.25)(X)
X = tf.keras.layers.Dense(256, activation='relu')(X)
X = tf.keras.layers.Dropout(0.25)(X)
X = tf.keras.layers.Dense(128, activation='relu')(X)
predictions = tf.keras.layers.Dense(N_CLASSES, activation='softmax')(X)
model = tf.keras.Model(inputs=model.input, outputs=predictions)
# for layer in model.layers:
# layer.trainable = True
# for layer in model.layers[15:]:
# layer.trainable = False
return model
Optimize, Compile, Train and Predict
optimizer=tf.keras.optimizers.Nadam(lr=2*1e-3, schedule_decay=1e-5)
algo = "inception_v3"
klass = "basics"
model = get_model(model_inception_v3)
opt = tf.keras.optimizers.Adam(lr=0.001, epsilon=1e-6)
model.compile(optimizer=optimizer, loss='categorical_crossentropy', metrics=['accuracy'])
model.summary()
Model: "model"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_2 (InputLayer) [(None, 150, 150, 3) 0
__________________________________________________________________________________________________
conv2d (Conv2D) (None, 74, 74, 32) 864 input_2[0][0]
__________________________________________________________________________________________________
batch_normalization (BatchNorma (None, 74, 74, 32) 96 conv2d[0][0]
__________________________________________________________________________________________________
activation_49 (Activation) (None, 74, 74, 32) 0 batch_normalization[0][0]
__________________________________________________________________________________________________
conv2d_1 (Conv2D) (None, 72, 72, 32) 9216 activation_49[0][0]
__________________________________________________________________________________________________
batch_normalization_1 (BatchNor (None, 72, 72, 32) 96 conv2d_1[0][0]
__________________________________________________________________________________________________
activation_50 (Activation) (None, 72, 72, 32) 0 batch_normalization_1[0][0]
__________________________________________________________________________________________________
conv2d_2 (Conv2D) (None, 72, 72, 64) 18432 activation_50[0][0]
__________________________________________________________________________________________________
batch_normalization_2 (BatchNor (None, 72, 72, 64) 192 conv2d_2[0][0]
__________________________________________________________________________________________________
activation_51 (Activation) (None, 72, 72, 64) 0 batch_normalization_2[0][0]
__________________________________________________________________________________________________
max_pooling2d_1 (MaxPooling2D) (None, 35, 35, 64) 0 activation_51[0][0]
__________________________________________________________________________________________________
conv2d_3 (Conv2D) (None, 35, 35, 80) 5120 max_pooling2d_1[0][0]
__________________________________________________________________________________________________
batch_normalization_3 (BatchNor (None, 35, 35, 80) 240 conv2d_3[0][0]
__________________________________________________________________________________________________
activation_52 (Activation) (None, 35, 35, 80) 0 batch_normalization_3[0][0]
__________________________________________________________________________________________________
conv2d_4 (Conv2D) (None, 33, 33, 192) 138240 activation_52[0][0]
__________________________________________________________________________________________________
batch_normalization_4 (BatchNor (None, 33, 33, 192) 576 conv2d_4[0][0]
__________________________________________________________________________________________________
activation_53 (Activation) (None, 33, 33, 192) 0 batch_normalization_4[0][0]
__________________________________________________________________________________________________
max_pooling2d_2 (MaxPooling2D) (None, 16, 16, 192) 0 activation_53[0][0]
__________________________________________________________________________________________________
conv2d_8 (Conv2D) (None, 16, 16, 64) 12288 max_pooling2d_2[0][0]
__________________________________________________________________________________________________
batch_normalization_8 (BatchNor (None, 16, 16, 64) 192 conv2d_8[0][0]
__________________________________________________________________________________________________
activation_57 (Activation) (None, 16, 16, 64) 0 batch_normalization_8[0][0]
__________________________________________________________________________________________________
conv2d_6 (Conv2D) (None, 16, 16, 48) 9216 max_pooling2d_2[0][0]
__________________________________________________________________________________________________
conv2d_9 (Conv2D) (None, 16, 16, 96) 55296 activation_57[0][0]
__________________________________________________________________________________________________
batch_normalization_6 (BatchNor (None, 16, 16, 48) 144 conv2d_6[0][0]
__________________________________________________________________________________________________
batch_normalization_9 (BatchNor (None, 16, 16, 96) 288 conv2d_9[0][0]
__________________________________________________________________________________________________
activation_55 (Activation) (None, 16, 16, 48) 0 batch_normalization_6[0][0]
__________________________________________________________________________________________________
activation_58 (Activation) (None, 16, 16, 96) 0 batch_normalization_9[0][0]
__________________________________________________________________________________________________
average_pooling2d (AveragePooli (None, 16, 16, 192) 0 max_pooling2d_2[0][0]
__________________________________________________________________________________________________
conv2d_5 (Conv2D) (None, 16, 16, 64) 12288 max_pooling2d_2[0][0]
__________________________________________________________________________________________________
conv2d_7 (Conv2D) (None, 16, 16, 64) 76800 activation_55[0][0]
__________________________________________________________________________________________________
conv2d_10 (Conv2D) (None, 16, 16, 96) 82944 activation_58[0][0]
__________________________________________________________________________________________________
conv2d_11 (Conv2D) (None, 16, 16, 32) 6144 average_pooling2d[0][0]
__________________________________________________________________________________________________
batch_normalization_5 (BatchNor (None, 16, 16, 64) 192 conv2d_5[0][0]
__________________________________________________________________________________________________
batch_normalization_7 (BatchNor (None, 16, 16, 64) 192 conv2d_7[0][0]
__________________________________________________________________________________________________
batch_normalization_10 (BatchNo (None, 16, 16, 96) 288 conv2d_10[0][0]
__________________________________________________________________________________________________
batch_normalization_11 (BatchNo (None, 16, 16, 32) 96 conv2d_11[0][0]
__________________________________________________________________________________________________
activation_54 (Activation) (None, 16, 16, 64) 0 batch_normalization_5[0][0]
__________________________________________________________________________________________________
activation_56 (Activation) (None, 16, 16, 64) 0 batch_normalization_7[0][0]
__________________________________________________________________________________________________
activation_59 (Activation) (None, 16, 16, 96) 0 batch_normalization_10[0][0]
__________________________________________________________________________________________________
activation_60 (Activation) (None, 16, 16, 32) 0 batch_normalization_11[0][0]
__________________________________________________________________________________________________
mixed0 (Concatenate) (None, 16, 16, 256) 0 activation_54[0][0]
activation_56[0][0]
activation_59[0][0]
activation_60[0][0]
__________________________________________________________________________________________________
conv2d_15 (Conv2D) (None, 16, 16, 64) 16384 mixed0[0][0]
__________________________________________________________________________________________________
batch_normalization_15 (BatchNo (None, 16, 16, 64) 192 conv2d_15[0][0]
__________________________________________________________________________________________________
activation_64 (Activation) (None, 16, 16, 64) 0 batch_normalization_15[0][0]
__________________________________________________________________________________________________
conv2d_13 (Conv2D) (None, 16, 16, 48) 12288 mixed0[0][0]
__________________________________________________________________________________________________
conv2d_16 (Conv2D) (None, 16, 16, 96) 55296 activation_64[0][0]
__________________________________________________________________________________________________
batch_normalization_13 (BatchNo (None, 16, 16, 48) 144 conv2d_13[0][0]
__________________________________________________________________________________________________
batch_normalization_16 (BatchNo (None, 16, 16, 96) 288 conv2d_16[0][0]
__________________________________________________________________________________________________
activation_62 (Activation) (None, 16, 16, 48) 0 batch_normalization_13[0][0]
__________________________________________________________________________________________________
activation_65 (Activation) (None, 16, 16, 96) 0 batch_normalization_16[0][0]
__________________________________________________________________________________________________
average_pooling2d_1 (AveragePoo (None, 16, 16, 256) 0 mixed0[0][0]
__________________________________________________________________________________________________
conv2d_12 (Conv2D) (None, 16, 16, 64) 16384 mixed0[0][0]
__________________________________________________________________________________________________
conv2d_14 (Conv2D) (None, 16, 16, 64) 76800 activation_62[0][0]
__________________________________________________________________________________________________
conv2d_17 (Conv2D) (None, 16, 16, 96) 82944 activation_65[0][0]
__________________________________________________________________________________________________
conv2d_18 (Conv2D) (None, 16, 16, 64) 16384 average_pooling2d_1[0][0]
__________________________________________________________________________________________________
batch_normalization_12 (BatchNo (None, 16, 16, 64) 192 conv2d_12[0][0]
__________________________________________________________________________________________________
batch_normalization_14 (BatchNo (None, 16, 16, 64) 192 conv2d_14[0][0]
__________________________________________________________________________________________________
batch_normalization_17 (BatchNo (None, 16, 16, 96) 288 conv2d_17[0][0]
__________________________________________________________________________________________________
batch_normalization_18 (BatchNo (None, 16, 16, 64) 192 conv2d_18[0][0]
__________________________________________________________________________________________________
activation_61 (Activation) (None, 16, 16, 64) 0 batch_normalization_12[0][0]
__________________________________________________________________________________________________
activation_63 (Activation) (None, 16, 16, 64) 0 batch_normalization_14[0][0]
__________________________________________________________________________________________________
activation_66 (Activation) (None, 16, 16, 96) 0 batch_normalization_17[0][0]
__________________________________________________________________________________________________
activation_67 (Activation) (None, 16, 16, 64) 0 batch_normalization_18[0][0]
__________________________________________________________________________________________________
mixed1 (Concatenate) (None, 16, 16, 288) 0 activation_61[0][0]
activation_63[0][0]
activation_66[0][0]
activation_67[0][0]
__________________________________________________________________________________________________
conv2d_22 (Conv2D) (None, 16, 16, 64) 18432 mixed1[0][0]
__________________________________________________________________________________________________
batch_normalization_22 (BatchNo (None, 16, 16, 64) 192 conv2d_22[0][0]
__________________________________________________________________________________________________
activation_71 (Activation) (None, 16, 16, 64) 0 batch_normalization_22[0][0]
__________________________________________________________________________________________________
conv2d_20 (Conv2D) (None, 16, 16, 48) 13824 mixed1[0][0]
__________________________________________________________________________________________________
conv2d_23 (Conv2D) (None, 16, 16, 96) 55296 activation_71[0][0]
__________________________________________________________________________________________________
batch_normalization_20 (BatchNo (None, 16, 16, 48) 144 conv2d_20[0][0]
__________________________________________________________________________________________________
batch_normalization_23 (BatchNo (None, 16, 16, 96) 288 conv2d_23[0][0]
__________________________________________________________________________________________________
activation_69 (Activation) (None, 16, 16, 48) 0 batch_normalization_20[0][0]
__________________________________________________________________________________________________
activation_72 (Activation) (None, 16, 16, 96) 0 batch_normalization_23[0][0]
__________________________________________________________________________________________________
average_pooling2d_2 (AveragePoo (None, 16, 16, 288) 0 mixed1[0][0]
__________________________________________________________________________________________________
conv2d_19 (Conv2D) (None, 16, 16, 64) 18432 mixed1[0][0]
__________________________________________________________________________________________________
conv2d_21 (Conv2D) (None, 16, 16, 64) 76800 activation_69[0][0]
__________________________________________________________________________________________________
conv2d_24 (Conv2D) (None, 16, 16, 96) 82944 activation_72[0][0]
__________________________________________________________________________________________________
conv2d_25 (Conv2D) (None, 16, 16, 64) 18432 average_pooling2d_2[0][0]
__________________________________________________________________________________________________
batch_normalization_19 (BatchNo (None, 16, 16, 64) 192 conv2d_19[0][0]
__________________________________________________________________________________________________
batch_normalization_21 (BatchNo (None, 16, 16, 64) 192 conv2d_21[0][0]
__________________________________________________________________________________________________
batch_normalization_24 (BatchNo (None, 16, 16, 96) 288 conv2d_24[0][0]
__________________________________________________________________________________________________
batch_normalization_25 (BatchNo (None, 16, 16, 64) 192 conv2d_25[0][0]
__________________________________________________________________________________________________
activation_68 (Activation) (None, 16, 16, 64) 0 batch_normalization_19[0][0]
__________________________________________________________________________________________________
activation_70 (Activation) (None, 16, 16, 64) 0 batch_normalization_21[0][0]
__________________________________________________________________________________________________
activation_73 (Activation) (None, 16, 16, 96) 0 batch_normalization_24[0][0]
__________________________________________________________________________________________________
activation_74 (Activation) (None, 16, 16, 64) 0 batch_normalization_25[0][0]
__________________________________________________________________________________________________
mixed2 (Concatenate) (None, 16, 16, 288) 0 activation_68[0][0]
activation_70[0][0]
activation_73[0][0]
activation_74[0][0]
__________________________________________________________________________________________________
conv2d_27 (Conv2D) (None, 16, 16, 64) 18432 mixed2[0][0]
__________________________________________________________________________________________________
batch_normalization_27 (BatchNo (None, 16, 16, 64) 192 conv2d_27[0][0]
__________________________________________________________________________________________________
activation_76 (Activation) (None, 16, 16, 64) 0 batch_normalization_27[0][0]
__________________________________________________________________________________________________
conv2d_28 (Conv2D) (None, 16, 16, 96) 55296 activation_76[0][0]
__________________________________________________________________________________________________
batch_normalization_28 (BatchNo (None, 16, 16, 96) 288 conv2d_28[0][0]
__________________________________________________________________________________________________
activation_77 (Activation) (None, 16, 16, 96) 0 batch_normalization_28[0][0]
__________________________________________________________________________________________________
conv2d_26 (Conv2D) (None, 7, 7, 384) 995328 mixed2[0][0]
__________________________________________________________________________________________________
conv2d_29 (Conv2D) (None, 7, 7, 96) 82944 activation_77[0][0]
__________________________________________________________________________________________________
batch_normalization_26 (BatchNo (None, 7, 7, 384) 1152 conv2d_26[0][0]
__________________________________________________________________________________________________
batch_normalization_29 (BatchNo (None, 7, 7, 96) 288 conv2d_29[0][0]
__________________________________________________________________________________________________
activation_75 (Activation) (None, 7, 7, 384) 0 batch_normalization_26[0][0]
__________________________________________________________________________________________________
activation_78 (Activation) (None, 7, 7, 96) 0 batch_normalization_29[0][0]
__________________________________________________________________________________________________
max_pooling2d_3 (MaxPooling2D) (None, 7, 7, 288) 0 mixed2[0][0]
__________________________________________________________________________________________________
mixed3 (Concatenate) (None, 7, 7, 768) 0 activation_75[0][0]
activation_78[0][0]
max_pooling2d_3[0][0]
__________________________________________________________________________________________________
conv2d_34 (Conv2D) (None, 7, 7, 128) 98304 mixed3[0][0]
__________________________________________________________________________________________________
batch_normalization_34 (BatchNo (None, 7, 7, 128) 384 conv2d_34[0][0]
__________________________________________________________________________________________________
activation_83 (Activation) (None, 7, 7, 128) 0 batch_normalization_34[0][0]
__________________________________________________________________________________________________
conv2d_35 (Conv2D) (None, 7, 7, 128) 114688 activation_83[0][0]
__________________________________________________________________________________________________
batch_normalization_35 (BatchNo (None, 7, 7, 128) 384 conv2d_35[0][0]
__________________________________________________________________________________________________
activation_84 (Activation) (None, 7, 7, 128) 0 batch_normalization_35[0][0]
__________________________________________________________________________________________________
conv2d_31 (Conv2D) (None, 7, 7, 128) 98304 mixed3[0][0]
__________________________________________________________________________________________________
conv2d_36 (Conv2D) (None, 7, 7, 128) 114688 activation_84[0][0]
__________________________________________________________________________________________________
batch_normalization_31 (BatchNo (None, 7, 7, 128) 384 conv2d_31[0][0]
__________________________________________________________________________________________________
batch_normalization_36 (BatchNo (None, 7, 7, 128) 384 conv2d_36[0][0]
__________________________________________________________________________________________________
activation_80 (Activation) (None, 7, 7, 128) 0 batch_normalization_31[0][0]
__________________________________________________________________________________________________
activation_85 (Activation) (None, 7, 7, 128) 0 batch_normalization_36[0][0]
__________________________________________________________________________________________________
conv2d_32 (Conv2D) (None, 7, 7, 128) 114688 activation_80[0][0]
__________________________________________________________________________________________________
conv2d_37 (Conv2D) (None, 7, 7, 128) 114688 activation_85[0][0]
__________________________________________________________________________________________________
batch_normalization_32 (BatchNo (None, 7, 7, 128) 384 conv2d_32[0][0]
__________________________________________________________________________________________________
batch_normalization_37 (BatchNo (None, 7, 7, 128) 384 conv2d_37[0][0]
__________________________________________________________________________________________________
activation_81 (Activation) (None, 7, 7, 128) 0 batch_normalization_32[0][0]
__________________________________________________________________________________________________
activation_86 (Activation) (None, 7, 7, 128) 0 batch_normalization_37[0][0]
__________________________________________________________________________________________________
average_pooling2d_3 (AveragePoo (None, 7, 7, 768) 0 mixed3[0][0]
__________________________________________________________________________________________________
conv2d_30 (Conv2D) (None, 7, 7, 192) 147456 mixed3[0][0]
__________________________________________________________________________________________________
conv2d_33 (Conv2D) (None, 7, 7, 192) 172032 activation_81[0][0]
__________________________________________________________________________________________________
conv2d_38 (Conv2D) (None, 7, 7, 192) 172032 activation_86[0][0]
__________________________________________________________________________________________________
conv2d_39 (Conv2D) (None, 7, 7, 192) 147456 average_pooling2d_3[0][0]
__________________________________________________________________________________________________
batch_normalization_30 (BatchNo (None, 7, 7, 192) 576 conv2d_30[0][0]
__________________________________________________________________________________________________
batch_normalization_33 (BatchNo (None, 7, 7, 192) 576 conv2d_33[0][0]
__________________________________________________________________________________________________
batch_normalization_38 (BatchNo (None, 7, 7, 192) 576 conv2d_38[0][0]
__________________________________________________________________________________________________
batch_normalization_39 (BatchNo (None, 7, 7, 192) 576 conv2d_39[0][0]
__________________________________________________________________________________________________
activation_79 (Activation) (None, 7, 7, 192) 0 batch_normalization_30[0][0]
__________________________________________________________________________________________________
activation_82 (Activation) (None, 7, 7, 192) 0 batch_normalization_33[0][0]
__________________________________________________________________________________________________
activation_87 (Activation) (None, 7, 7, 192) 0 batch_normalization_38[0][0]
__________________________________________________________________________________________________
activation_88 (Activation) (None, 7, 7, 192) 0 batch_normalization_39[0][0]
__________________________________________________________________________________________________
mixed4 (Concatenate) (None, 7, 7, 768) 0 activation_79[0][0]
activation_82[0][0]
activation_87[0][0]
activation_88[0][0]
__________________________________________________________________________________________________
conv2d_44 (Conv2D) (None, 7, 7, 160) 122880 mixed4[0][0]
__________________________________________________________________________________________________
batch_normalization_44 (BatchNo (None, 7, 7, 160) 480 conv2d_44[0][0]
__________________________________________________________________________________________________
activation_93 (Activation) (None, 7, 7, 160) 0 batch_normalization_44[0][0]
__________________________________________________________________________________________________
conv2d_45 (Conv2D) (None, 7, 7, 160) 179200 activation_93[0][0]
__________________________________________________________________________________________________
batch_normalization_45 (BatchNo (None, 7, 7, 160) 480 conv2d_45[0][0]
__________________________________________________________________________________________________
activation_94 (Activation) (None, 7, 7, 160) 0 batch_normalization_45[0][0]
__________________________________________________________________________________________________
conv2d_41 (Conv2D) (None, 7, 7, 160) 122880 mixed4[0][0]
__________________________________________________________________________________________________
conv2d_46 (Conv2D) (None, 7, 7, 160) 179200 activation_94[0][0]
__________________________________________________________________________________________________
batch_normalization_41 (BatchNo (None, 7, 7, 160) 480 conv2d_41[0][0]
__________________________________________________________________________________________________
batch_normalization_46 (BatchNo (None, 7, 7, 160) 480 conv2d_46[0][0]
__________________________________________________________________________________________________
activation_90 (Activation) (None, 7, 7, 160) 0 batch_normalization_41[0][0]
__________________________________________________________________________________________________
activation_95 (Activation) (None, 7, 7, 160) 0 batch_normalization_46[0][0]
__________________________________________________________________________________________________
conv2d_42 (Conv2D) (None, 7, 7, 160) 179200 activation_90[0][0]
__________________________________________________________________________________________________
conv2d_47 (Conv2D) (None, 7, 7, 160) 179200 activation_95[0][0]
__________________________________________________________________________________________________
batch_normalization_42 (BatchNo (None, 7, 7, 160) 480 conv2d_42[0][0]
__________________________________________________________________________________________________
batch_normalization_47 (BatchNo (None, 7, 7, 160) 480 conv2d_47[0][0]
__________________________________________________________________________________________________
activation_91 (Activation) (None, 7, 7, 160) 0 batch_normalization_42[0][0]
__________________________________________________________________________________________________
activation_96 (Activation) (None, 7, 7, 160) 0 batch_normalization_47[0][0]
__________________________________________________________________________________________________
average_pooling2d_4 (AveragePoo (None, 7, 7, 768) 0 mixed4[0][0]
__________________________________________________________________________________________________
conv2d_40 (Conv2D) (None, 7, 7, 192) 147456 mixed4[0][0]
__________________________________________________________________________________________________
conv2d_43 (Conv2D) (None, 7, 7, 192) 215040 activation_91[0][0]
__________________________________________________________________________________________________
conv2d_48 (Conv2D) (None, 7, 7, 192) 215040 activation_96[0][0]
__________________________________________________________________________________________________
conv2d_49 (Conv2D) (None, 7, 7, 192) 147456 average_pooling2d_4[0][0]
__________________________________________________________________________________________________
batch_normalization_40 (BatchNo (None, 7, 7, 192) 576 conv2d_40[0][0]
__________________________________________________________________________________________________
batch_normalization_43 (BatchNo (None, 7, 7, 192) 576 conv2d_43[0][0]
__________________________________________________________________________________________________
batch_normalization_48 (BatchNo (None, 7, 7, 192) 576 conv2d_48[0][0]
__________________________________________________________________________________________________
batch_normalization_49 (BatchNo (None, 7, 7, 192) 576 conv2d_49[0][0]
__________________________________________________________________________________________________
activation_89 (Activation) (None, 7, 7, 192) 0 batch_normalization_40[0][0]
__________________________________________________________________________________________________
activation_92 (Activation) (None, 7, 7, 192) 0 batch_normalization_43[0][0]
__________________________________________________________________________________________________
activation_97 (Activation) (None, 7, 7, 192) 0 batch_normalization_48[0][0]
__________________________________________________________________________________________________
activation_98 (Activation) (None, 7, 7, 192) 0 batch_normalization_49[0][0]
__________________________________________________________________________________________________
mixed5 (Concatenate) (None, 7, 7, 768) 0 activation_89[0][0]
activation_92[0][0]
activation_97[0][0]
activation_98[0][0]
__________________________________________________________________________________________________
conv2d_54 (Conv2D) (None, 7, 7, 160) 122880 mixed5[0][0]
__________________________________________________________________________________________________
batch_normalization_54 (BatchNo (None, 7, 7, 160) 480 conv2d_54[0][0]
__________________________________________________________________________________________________
activation_103 (Activation) (None, 7, 7, 160) 0 batch_normalization_54[0][0]
__________________________________________________________________________________________________
conv2d_55 (Conv2D) (None, 7, 7, 160) 179200 activation_103[0][0]
__________________________________________________________________________________________________
batch_normalization_55 (BatchNo (None, 7, 7, 160) 480 conv2d_55[0][0]
__________________________________________________________________________________________________
activation_104 (Activation) (None, 7, 7, 160) 0 batch_normalization_55[0][0]
__________________________________________________________________________________________________
conv2d_51 (Conv2D) (None, 7, 7, 160) 122880 mixed5[0][0]
__________________________________________________________________________________________________
conv2d_56 (Conv2D) (None, 7, 7, 160) 179200 activation_104[0][0]
__________________________________________________________________________________________________
batch_normalization_51 (BatchNo (None, 7, 7, 160) 480 conv2d_51[0][0]
__________________________________________________________________________________________________
batch_normalization_56 (BatchNo (None, 7, 7, 160) 480 conv2d_56[0][0]
__________________________________________________________________________________________________
activation_100 (Activation) (None, 7, 7, 160) 0 batch_normalization_51[0][0]
__________________________________________________________________________________________________
activation_105 (Activation) (None, 7, 7, 160) 0 batch_normalization_56[0][0]
__________________________________________________________________________________________________
conv2d_52 (Conv2D) (None, 7, 7, 160) 179200 activation_100[0][0]
__________________________________________________________________________________________________
conv2d_57 (Conv2D) (None, 7, 7, 160) 179200 activation_105[0][0]
__________________________________________________________________________________________________
batch_normalization_52 (BatchNo (None, 7, 7, 160) 480 conv2d_52[0][0]
__________________________________________________________________________________________________
batch_normalization_57 (BatchNo (None, 7, 7, 160) 480 conv2d_57[0][0]
__________________________________________________________________________________________________
activation_101 (Activation) (None, 7, 7, 160) 0 batch_normalization_52[0][0]
__________________________________________________________________________________________________
activation_106 (Activation) (None, 7, 7, 160) 0 batch_normalization_57[0][0]
__________________________________________________________________________________________________
average_pooling2d_5 (AveragePoo (None, 7, 7, 768) 0 mixed5[0][0]
__________________________________________________________________________________________________
conv2d_50 (Conv2D) (None, 7, 7, 192) 147456 mixed5[0][0]
__________________________________________________________________________________________________
conv2d_53 (Conv2D) (None, 7, 7, 192) 215040 activation_101[0][0]
__________________________________________________________________________________________________
conv2d_58 (Conv2D) (None, 7, 7, 192) 215040 activation_106[0][0]
__________________________________________________________________________________________________
conv2d_59 (Conv2D) (None, 7, 7, 192) 147456 average_pooling2d_5[0][0]
__________________________________________________________________________________________________
batch_normalization_50 (BatchNo (None, 7, 7, 192) 576 conv2d_50[0][0]
__________________________________________________________________________________________________
batch_normalization_53 (BatchNo (None, 7, 7, 192) 576 conv2d_53[0][0]
__________________________________________________________________________________________________
batch_normalization_58 (BatchNo (None, 7, 7, 192) 576 conv2d_58[0][0]
__________________________________________________________________________________________________
batch_normalization_59 (BatchNo (None, 7, 7, 192) 576 conv2d_59[0][0]
__________________________________________________________________________________________________
activation_99 (Activation) (None, 7, 7, 192) 0 batch_normalization_50[0][0]
__________________________________________________________________________________________________
activation_102 (Activation) (None, 7, 7, 192) 0 batch_normalization_53[0][0]
__________________________________________________________________________________________________
activation_107 (Activation) (None, 7, 7, 192) 0 batch_normalization_58[0][0]
__________________________________________________________________________________________________
activation_108 (Activation) (None, 7, 7, 192) 0 batch_normalization_59[0][0]
__________________________________________________________________________________________________
mixed6 (Concatenate) (None, 7, 7, 768) 0 activation_99[0][0]
activation_102[0][0]
activation_107[0][0]
activation_108[0][0]
__________________________________________________________________________________________________
conv2d_64 (Conv2D) (None, 7, 7, 192) 147456 mixed6[0][0]
__________________________________________________________________________________________________
batch_normalization_64 (BatchNo (None, 7, 7, 192) 576 conv2d_64[0][0]
__________________________________________________________________________________________________
activation_113 (Activation) (None, 7, 7, 192) 0 batch_normalization_64[0][0]
__________________________________________________________________________________________________
conv2d_65 (Conv2D) (None, 7, 7, 192) 258048 activation_113[0][0]
__________________________________________________________________________________________________
batch_normalization_65 (BatchNo (None, 7, 7, 192) 576 conv2d_65[0][0]
__________________________________________________________________________________________________
activation_114 (Activation) (None, 7, 7, 192) 0 batch_normalization_65[0][0]
__________________________________________________________________________________________________
conv2d_61 (Conv2D) (None, 7, 7, 192) 147456 mixed6[0][0]
__________________________________________________________________________________________________
conv2d_66 (Conv2D) (None, 7, 7, 192) 258048 activation_114[0][0]
__________________________________________________________________________________________________
batch_normalization_61 (BatchNo (None, 7, 7, 192) 576 conv2d_61[0][0]
__________________________________________________________________________________________________
batch_normalization_66 (BatchNo (None, 7, 7, 192) 576 conv2d_66[0][0]
__________________________________________________________________________________________________
activation_110 (Activation) (None, 7, 7, 192) 0 batch_normalization_61[0][0]
__________________________________________________________________________________________________
activation_115 (Activation) (None, 7, 7, 192) 0 batch_normalization_66[0][0]
__________________________________________________________________________________________________
conv2d_62 (Conv2D) (None, 7, 7, 192) 258048 activation_110[0][0]
__________________________________________________________________________________________________
conv2d_67 (Conv2D) (None, 7, 7, 192) 258048 activation_115[0][0]
__________________________________________________________________________________________________
batch_normalization_62 (BatchNo (None, 7, 7, 192) 576 conv2d_62[0][0]
__________________________________________________________________________________________________
batch_normalization_67 (BatchNo (None, 7, 7, 192) 576 conv2d_67[0][0]
__________________________________________________________________________________________________
activation_111 (Activation) (None, 7, 7, 192) 0 batch_normalization_62[0][0]
__________________________________________________________________________________________________
activation_116 (Activation) (None, 7, 7, 192) 0 batch_normalization_67[0][0]
__________________________________________________________________________________________________
average_pooling2d_6 (AveragePoo (None, 7, 7, 768) 0 mixed6[0][0]
__________________________________________________________________________________________________
conv2d_60 (Conv2D) (None, 7, 7, 192) 147456 mixed6[0][0]
__________________________________________________________________________________________________
conv2d_63 (Conv2D) (None, 7, 7, 192) 258048 activation_111[0][0]
__________________________________________________________________________________________________
conv2d_68 (Conv2D) (None, 7, 7, 192) 258048 activation_116[0][0]
__________________________________________________________________________________________________
conv2d_69 (Conv2D) (None, 7, 7, 192) 147456 average_pooling2d_6[0][0]
__________________________________________________________________________________________________
batch_normalization_60 (BatchNo (None, 7, 7, 192) 576 conv2d_60[0][0]
__________________________________________________________________________________________________
batch_normalization_63 (BatchNo (None, 7, 7, 192) 576 conv2d_63[0][0]
__________________________________________________________________________________________________
batch_normalization_68 (BatchNo (None, 7, 7, 192) 576 conv2d_68[0][0]
__________________________________________________________________________________________________
batch_normalization_69 (BatchNo (None, 7, 7, 192) 576 conv2d_69[0][0]
__________________________________________________________________________________________________
activation_109 (Activation) (None, 7, 7, 192) 0 batch_normalization_60[0][0]
__________________________________________________________________________________________________
activation_112 (Activation) (None, 7, 7, 192) 0 batch_normalization_63[0][0]
__________________________________________________________________________________________________
activation_117 (Activation) (None, 7, 7, 192) 0 batch_normalization_68[0][0]
__________________________________________________________________________________________________
activation_118 (Activation) (None, 7, 7, 192) 0 batch_normalization_69[0][0]
__________________________________________________________________________________________________
mixed7 (Concatenate) (None, 7, 7, 768) 0 activation_109[0][0]
activation_112[0][0]
activation_117[0][0]
activation_118[0][0]
__________________________________________________________________________________________________
conv2d_72 (Conv2D) (None, 7, 7, 192) 147456 mixed7[0][0]
__________________________________________________________________________________________________
batch_normalization_72 (BatchNo (None, 7, 7, 192) 576 conv2d_72[0][0]
__________________________________________________________________________________________________
activation_121 (Activation) (None, 7, 7, 192) 0 batch_normalization_72[0][0]
__________________________________________________________________________________________________
conv2d_73 (Conv2D) (None, 7, 7, 192) 258048 activation_121[0][0]
__________________________________________________________________________________________________
batch_normalization_73 (BatchNo (None, 7, 7, 192) 576 conv2d_73[0][0]
__________________________________________________________________________________________________
activation_122 (Activation) (None, 7, 7, 192) 0 batch_normalization_73[0][0]
__________________________________________________________________________________________________
conv2d_70 (Conv2D) (None, 7, 7, 192) 147456 mixed7[0][0]
__________________________________________________________________________________________________
conv2d_74 (Conv2D) (None, 7, 7, 192) 258048 activation_122[0][0]
__________________________________________________________________________________________________
batch_normalization_70 (BatchNo (None, 7, 7, 192) 576 conv2d_70[0][0]
__________________________________________________________________________________________________
batch_normalization_74 (BatchNo (None, 7, 7, 192) 576 conv2d_74[0][0]
__________________________________________________________________________________________________
activation_119 (Activation) (None, 7, 7, 192) 0 batch_normalization_70[0][0]
__________________________________________________________________________________________________
activation_123 (Activation) (None, 7, 7, 192) 0 batch_normalization_74[0][0]
__________________________________________________________________________________________________
conv2d_71 (Conv2D) (None, 3, 3, 320) 552960 activation_119[0][0]
__________________________________________________________________________________________________
conv2d_75 (Conv2D) (None, 3, 3, 192) 331776 activation_123[0][0]
__________________________________________________________________________________________________
batch_normalization_71 (BatchNo (None, 3, 3, 320) 960 conv2d_71[0][0]
__________________________________________________________________________________________________
batch_normalization_75 (BatchNo (None, 3, 3, 192) 576 conv2d_75[0][0]
__________________________________________________________________________________________________
activation_120 (Activation) (None, 3, 3, 320) 0 batch_normalization_71[0][0]
__________________________________________________________________________________________________
activation_124 (Activation) (None, 3, 3, 192) 0 batch_normalization_75[0][0]
__________________________________________________________________________________________________
max_pooling2d_4 (MaxPooling2D) (None, 3, 3, 768) 0 mixed7[0][0]
__________________________________________________________________________________________________
mixed8 (Concatenate) (None, 3, 3, 1280) 0 activation_120[0][0]
activation_124[0][0]
max_pooling2d_4[0][0]
__________________________________________________________________________________________________
conv2d_80 (Conv2D) (None, 3, 3, 448) 573440 mixed8[0][0]
__________________________________________________________________________________________________
batch_normalization_80 (BatchNo (None, 3, 3, 448) 1344 conv2d_80[0][0]
__________________________________________________________________________________________________
activation_129 (Activation) (None, 3, 3, 448) 0 batch_normalization_80[0][0]
__________________________________________________________________________________________________
conv2d_77 (Conv2D) (None, 3, 3, 384) 491520 mixed8[0][0]
__________________________________________________________________________________________________
conv2d_81 (Conv2D) (None, 3, 3, 384) 1548288 activation_129[0][0]
__________________________________________________________________________________________________
batch_normalization_77 (BatchNo (None, 3, 3, 384) 1152 conv2d_77[0][0]
__________________________________________________________________________________________________
batch_normalization_81 (BatchNo (None, 3, 3, 384) 1152 conv2d_81[0][0]
__________________________________________________________________________________________________
activation_126 (Activation) (None, 3, 3, 384) 0 batch_normalization_77[0][0]
__________________________________________________________________________________________________
activation_130 (Activation) (None, 3, 3, 384) 0 batch_normalization_81[0][0]
__________________________________________________________________________________________________
conv2d_78 (Conv2D) (None, 3, 3, 384) 442368 activation_126[0][0]
__________________________________________________________________________________________________
conv2d_79 (Conv2D) (None, 3, 3, 384) 442368 activation_126[0][0]
__________________________________________________________________________________________________
conv2d_82 (Conv2D) (None, 3, 3, 384) 442368 activation_130[0][0]
__________________________________________________________________________________________________
conv2d_83 (Conv2D) (None, 3, 3, 384) 442368 activation_130[0][0]
__________________________________________________________________________________________________
average_pooling2d_7 (AveragePoo (None, 3, 3, 1280) 0 mixed8[0][0]
__________________________________________________________________________________________________
conv2d_76 (Conv2D) (None, 3, 3, 320) 409600 mixed8[0][0]
__________________________________________________________________________________________________
batch_normalization_78 (BatchNo (None, 3, 3, 384) 1152 conv2d_78[0][0]
__________________________________________________________________________________________________
batch_normalization_79 (BatchNo (None, 3, 3, 384) 1152 conv2d_79[0][0]
__________________________________________________________________________________________________
batch_normalization_82 (BatchNo (None, 3, 3, 384) 1152 conv2d_82[0][0]
__________________________________________________________________________________________________
batch_normalization_83 (BatchNo (None, 3, 3, 384) 1152 conv2d_83[0][0]
__________________________________________________________________________________________________
conv2d_84 (Conv2D) (None, 3, 3, 192) 245760 average_pooling2d_7[0][0]
__________________________________________________________________________________________________
batch_normalization_76 (BatchNo (None, 3, 3, 320) 960 conv2d_76[0][0]
__________________________________________________________________________________________________
activation_127 (Activation) (None, 3, 3, 384) 0 batch_normalization_78[0][0]
__________________________________________________________________________________________________
activation_128 (Activation) (None, 3, 3, 384) 0 batch_normalization_79[0][0]
__________________________________________________________________________________________________
activation_131 (Activation) (None, 3, 3, 384) 0 batch_normalization_82[0][0]
__________________________________________________________________________________________________
activation_132 (Activation) (None, 3, 3, 384) 0 batch_normalization_83[0][0]
__________________________________________________________________________________________________
batch_normalization_84 (BatchNo (None, 3, 3, 192) 576 conv2d_84[0][0]
__________________________________________________________________________________________________
activation_125 (Activation) (None, 3, 3, 320) 0 batch_normalization_76[0][0]
__________________________________________________________________________________________________
mixed9_0 (Concatenate) (None, 3, 3, 768) 0 activation_127[0][0]
activation_128[0][0]
__________________________________________________________________________________________________
concatenate (Concatenate) (None, 3, 3, 768) 0 activation_131[0][0]
activation_132[0][0]
__________________________________________________________________________________________________
activation_133 (Activation) (None, 3, 3, 192) 0 batch_normalization_84[0][0]
__________________________________________________________________________________________________
mixed9 (Concatenate) (None, 3, 3, 2048) 0 activation_125[0][0]
mixed9_0[0][0]
concatenate[0][0]
activation_133[0][0]
__________________________________________________________________________________________________
conv2d_89 (Conv2D) (None, 3, 3, 448) 917504 mixed9[0][0]
__________________________________________________________________________________________________
batch_normalization_89 (BatchNo (None, 3, 3, 448) 1344 conv2d_89[0][0]
__________________________________________________________________________________________________
activation_138 (Activation) (None, 3, 3, 448) 0 batch_normalization_89[0][0]
__________________________________________________________________________________________________
conv2d_86 (Conv2D) (None, 3, 3, 384) 786432 mixed9[0][0]
__________________________________________________________________________________________________
conv2d_90 (Conv2D) (None, 3, 3, 384) 1548288 activation_138[0][0]
__________________________________________________________________________________________________
batch_normalization_86 (BatchNo (None, 3, 3, 384) 1152 conv2d_86[0][0]
__________________________________________________________________________________________________
batch_normalization_90 (BatchNo (None, 3, 3, 384) 1152 conv2d_90[0][0]
__________________________________________________________________________________________________
activation_135 (Activation) (None, 3, 3, 384) 0 batch_normalization_86[0][0]
__________________________________________________________________________________________________
activation_139 (Activation) (None, 3, 3, 384) 0 batch_normalization_90[0][0]
__________________________________________________________________________________________________
conv2d_87 (Conv2D) (None, 3, 3, 384) 442368 activation_135[0][0]
__________________________________________________________________________________________________
conv2d_88 (Conv2D) (None, 3, 3, 384) 442368 activation_135[0][0]
__________________________________________________________________________________________________
conv2d_91 (Conv2D) (None, 3, 3, 384) 442368 activation_139[0][0]
__________________________________________________________________________________________________
conv2d_92 (Conv2D) (None, 3, 3, 384) 442368 activation_139[0][0]
__________________________________________________________________________________________________
average_pooling2d_8 (AveragePoo (None, 3, 3, 2048) 0 mixed9[0][0]
__________________________________________________________________________________________________
conv2d_85 (Conv2D) (None, 3, 3, 320) 655360 mixed9[0][0]
__________________________________________________________________________________________________
batch_normalization_87 (BatchNo (None, 3, 3, 384) 1152 conv2d_87[0][0]
__________________________________________________________________________________________________
batch_normalization_88 (BatchNo (None, 3, 3, 384) 1152 conv2d_88[0][0]
__________________________________________________________________________________________________
batch_normalization_91 (BatchNo (None, 3, 3, 384) 1152 conv2d_91[0][0]
__________________________________________________________________________________________________
batch_normalization_92 (BatchNo (None, 3, 3, 384) 1152 conv2d_92[0][0]
__________________________________________________________________________________________________
conv2d_93 (Conv2D) (None, 3, 3, 192) 393216 average_pooling2d_8[0][0]
__________________________________________________________________________________________________
batch_normalization_85 (BatchNo (None, 3, 3, 320) 960 conv2d_85[0][0]
__________________________________________________________________________________________________
activation_136 (Activation) (None, 3, 3, 384) 0 batch_normalization_87[0][0]
__________________________________________________________________________________________________
activation_137 (Activation) (None, 3, 3, 384) 0 batch_normalization_88[0][0]
__________________________________________________________________________________________________
activation_140 (Activation) (None, 3, 3, 384) 0 batch_normalization_91[0][0]
__________________________________________________________________________________________________
activation_141 (Activation) (None, 3, 3, 384) 0 batch_normalization_92[0][0]
__________________________________________________________________________________________________
batch_normalization_93 (BatchNo (None, 3, 3, 192) 576 conv2d_93[0][0]
__________________________________________________________________________________________________
activation_134 (Activation) (None, 3, 3, 320) 0 batch_normalization_85[0][0]
__________________________________________________________________________________________________
mixed9_1 (Concatenate) (None, 3, 3, 768) 0 activation_136[0][0]
activation_137[0][0]
__________________________________________________________________________________________________
concatenate_1 (Concatenate) (None, 3, 3, 768) 0 activation_140[0][0]
activation_141[0][0]
__________________________________________________________________________________________________
activation_142 (Activation) (None, 3, 3, 192) 0 batch_normalization_93[0][0]
__________________________________________________________________________________________________
mixed10 (Concatenate) (None, 3, 3, 2048) 0 activation_134[0][0]
mixed9_1[0][0]
concatenate_1[0][0]
activation_142[0][0]
__________________________________________________________________________________________________
global_average_pooling2d (Globa (None, 2048) 0 mixed10[0][0]
__________________________________________________________________________________________________
dense (Dense) (None, 2048) 4196352 global_average_pooling2d[0][0]
__________________________________________________________________________________________________
dropout (Dropout) (None, 2048) 0 dense[0][0]
__________________________________________________________________________________________________
dense_1 (Dense) (None, 1024) 2098176 dropout[0][0]
__________________________________________________________________________________________________
dropout_1 (Dropout) (None, 1024) 0 dense_1[0][0]
__________________________________________________________________________________________________
dense_2 (Dense) (None, 512) 524800 dropout_1[0][0]
__________________________________________________________________________________________________
dropout_2 (Dropout) (None, 512) 0 dense_2[0][0]
__________________________________________________________________________________________________
dense_3 (Dense) (None, 256) 131328 dropout_2[0][0]
__________________________________________________________________________________________________
dropout_3 (Dropout) (None, 256) 0 dense_3[0][0]
__________________________________________________________________________________________________
dense_4 (Dense) (None, 128) 32896 dropout_3[0][0]
__________________________________________________________________________________________________
dense_5 (Dense) (None, 5) 645 dense_4[0][0]
==================================================================================================
Total params: 28,786,981
Trainable params: 28,752,549
Non-trainable params: 34,432
__________________________________________________________________________________________________
from sklearn.metrics import cohen_kappa_score
class QWKCallback(tf.keras.callbacks.Callback):
def __init__(self, validation_data):
super(tf.keras.callbacks.Callback, self).__init__()
self.X = validation_data[0]
self.Y = validation_data[1]
self.history = []
def on_epoch_end(self, epoch, logs={}):
pred = self.model.predict(self.X)
score = cohen_kappa_score(
np.argmax(self.Y, axis=1), np.argmax(pred, axis=1), labels=[0, 1, 2, 3, 4], weights="quadratic"
)
print(("Epoch {0} : QWK : {1}".format(epoch, score)))
self.history.append(score)
if(score >= max(self.history)):
print("Saving Checkpoint: {0}".format(score))
self.model.save("../Resnet50_bestQWK.h5")
early_stopping = tf.keras.callbacks.EarlyStopping(monitor="val_loss",
min_delta=0.0001, patience=3, verbose=1, mode="auto")
reduce_lr = tf.keras.callbacks.ReduceLROnPlateau(monitor="val_loss",
min_delta=0.0004, patience=2, factor=0.1, min_lr=1e-6, mode="auto", verbose=1)
qwk = QWKCallback(validation_data)
model.fit_generator(
generator=train_data,
#steps_per_epochs=EPOCH_STEPS,
#batch_size=BATCH_SIZE,
epochs=EPOCHS,
validation_data=validation_data,
validation_steps=30#,
#callbacks=[early_stopping, reduce_lr]
)
/opt/conda/lib/python3.6/site-packages/keras_preprocessing/image/image_data_generator.py:716: UserWarning: This ImageDataGenerator specifies `featurewise_center`, but it hasn't been fit on any training data. Fit it first by calling `.fit(numpy_data)`.
warnings.warn('This ImageDataGenerator specifies '
/opt/conda/lib/python3.6/site-packages/keras_preprocessing/image/image_data_generator.py:724: UserWarning: This ImageDataGenerator specifies `featurewise_std_normalization`, but it hasn't been fit on any training data. Fit it first by calling `.fit(numpy_data)`.
warnings.warn('This ImageDataGenerator specifies '
Epoch 1/25
/opt/conda/lib/python3.6/site-packages/keras_preprocessing/image/image_data_generator.py:716: UserWarning: This ImageDataGenerator specifies `featurewise_center`, but it hasn't been fit on any training data. Fit it first by calling `.fit(numpy_data)`.
warnings.warn('This ImageDataGenerator specifies '
/opt/conda/lib/python3.6/site-packages/keras_preprocessing/image/image_data_generator.py:724: UserWarning: This ImageDataGenerator specifies `featurewise_std_normalization`, but it hasn't been fit on any training data. Fit it first by calling `.fit(numpy_data)`.
warnings.warn('This ImageDataGenerator specifies '
103/103 [==============================] - 503s 5s/step - loss: 1.2704 - acc: 0.5507 - val_loss: 3530.0762 - val_acc: 0.4816
Epoch 2/25
103/103 [==============================] - 448s 4s/step - loss: 1.0078 - acc: 0.6748 - val_loss: 21.8471 - val_acc: 0.4946
Epoch 3/25
103/103 [==============================] - 440s 4s/step - loss: 0.9933 - acc: 0.6663 - val_loss: 87.6621 - val_acc: 0.4903
Epoch 4/25
103/103 [==============================] - 443s 4s/step - loss: 0.9498 - acc: 0.6878 - val_loss: 190.1645 - val_acc: 0.5444
Epoch 5/25
103/103 [==============================] - 445s 4s/step - loss: 0.8962 - acc: 0.6878 - val_loss: 1022.9081 - val_acc: 0.1374
Epoch 6/25
103/103 [==============================] - 441s 4s/step - loss: 0.9300 - acc: 0.6854 - val_loss: 965.7076 - val_acc: 0.5584
Epoch 7/25
103/103 [==============================] - 448s 4s/step - loss: 0.9197 - acc: 0.6893 - val_loss: 14.3867 - val_acc: 0.4968
Epoch 8/25
103/103 [==============================] - 444s 4s/step - loss: 0.8321 - acc: 0.7127 - val_loss: 1.2525 - val_acc: 0.7100
Epoch 9/25
103/103 [==============================] - 448s 4s/step - loss: 0.8426 - acc: 0.7042 - val_loss: 1.7017 - val_acc: 0.6115
Epoch 10/25
103/103 [==============================] - 445s 4s/step - loss: 0.8222 - acc: 0.7130 - val_loss: 8.9885 - val_acc: 0.6450
Epoch 11/25
103/103 [==============================] - 437s 4s/step - loss: 0.8559 - acc: 0.7133 - val_loss: 202.3459 - val_acc: 0.5032
Epoch 12/25
103/103 [==============================] - 441s 4s/step - loss: 0.8583 - acc: 0.7118 - val_loss: 1.1618 - val_acc: 0.6645
Epoch 13/25
103/103 [==============================] - 447s 4s/step - loss: 0.8247 - acc: 0.7166 - val_loss: 1.2152 - val_acc: 0.7132
Epoch 14/25
103/103 [==============================] - 446s 4s/step - loss: 1.0001 - acc: 0.6602 - val_loss: 830.5764 - val_acc: 0.4329
Epoch 15/25
103/103 [==============================] - 447s 4s/step - loss: 0.9909 - acc: 0.6602 - val_loss: 43.9164 - val_acc: 0.5216
Epoch 16/25
103/103 [==============================] - 446s 4s/step - loss: 0.8771 - acc: 0.6969 - val_loss: 1.4656 - val_acc: 0.6418
Epoch 17/25
103/103 [==============================] - 448s 4s/step - loss: 0.8122 - acc: 0.7175 - val_loss: 1.0396 - val_acc: 0.6948
Epoch 18/25
103/103 [==============================] - 441s 4s/step - loss: 0.8221 - acc: 0.7118 - val_loss: 0.8343 - val_acc: 0.7284
Epoch 19/25
103/103 [==============================] - 447s 4s/step - loss: 0.7953 - acc: 0.7148 - val_loss: 1.1735 - val_acc: 0.6721
Epoch 20/25
103/103 [==============================] - 444s 4s/step - loss: 0.8761 - acc: 0.6990 - val_loss: 4.6178 - val_acc: 0.3831
Epoch 21/25
103/103 [==============================] - 442s 4s/step - loss: 0.8527 - acc: 0.7042 - val_loss: 1.2654 - val_acc: 0.6331
Epoch 22/25
103/103 [==============================] - 445s 4s/step - loss: 0.8787 - acc: 0.6984 - val_loss: 5.4739 - val_acc: 0.6699
Epoch 23/25
103/103 [==============================] - 439s 4s/step - loss: 0.8281 - acc: 0.7148 - val_loss: 0.8836 - val_acc: 0.6894
Epoch 24/25
103/103 [==============================] - 438s 4s/step - loss: 0.7883 - acc: 0.7212 - val_loss: 0.7943 - val_acc: 0.7045
Epoch 25/25
103/103 [==============================] - 438s 4s/step - loss: 0.8209 - acc: 0.7093 - val_loss: 1.5930 - val_acc: 0.6061
<tensorflow.python.keras.callbacks.History at 0x7fb2dac07c50>
filenames = test_data.filenames
classifications = model.predict_generator(test_data, steps=len(filenames))
results = pd.DataFrame({
"id_code": filenames,
"diagnosis": np.argmax(classifications, axis=1)
})
results["id_code"] = results["id_code"].map(lambda x: str(x)[:-4].split("/")[4])
results.head()
| id_code | diagnosis | |
|---|---|---|
| 0 | 0005cfc8afb6 | 0 |
| 1 | 003f0afdcd15 | 0 |
| 2 | 006efc72b638 | 0 |
| 3 | 00836aaacf06 | 0 |
| 4 | 009245722fa4 | 0 |
file_name = "{0}_{1}.csv".format(algo, klass)
results.to_csv("submission.csv", index=False)
results.diagnosis.value_counts()
len(model.layers)
322