236 KiB
236 KiB
Aleksandra Jonas, Aleksandra Gronowska, Iwona Christop
Zadanie 9-10, zadanie 1 - VGG16 + ResNet on train_test_sw
Przygotowanie danych
from IPython.display import Image, displayimport sys
import subprocess
import pkg_resources
import numpy as np
required = { 'scikit-image'}
installed = {pkg.key for pkg in pkg_resources.working_set}
missing = required - installed
# VGG16 requires images to be of dim = (224, 224, 3)
newSize = (224,224)
if missing:
python = sys.executable
subprocess.check_call([python, '-m', 'pip', 'install', *missing], stdout=subprocess.DEVNULL)
def load_train_data(input_dir):
import numpy as np
import pandas as pd
import os
from skimage.io import imread
import cv2 as cv
from pathlib import Path
import random
from shutil import copyfile, rmtree
import json
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib
image_dir = Path(input_dir)
categories_name = []
for file in os.listdir(image_dir):
d = os.path.join(image_dir, file)
if os.path.isdir(d):
categories_name.append(file)
folders = [directory for directory in image_dir.iterdir() if directory.is_dir()]
train_img = []
categories_count=[]
labels=[]
for i, direc in enumerate(folders):
count = 0
for obj in direc.iterdir():
if os.path.isfile(obj) and os.path.basename(os.path.normpath(obj)) != 'desktop.ini':
labels.append(os.path.basename(os.path.normpath(direc)))
count += 1
img = imread(obj)#zwraca ndarry postaci xSize x ySize x colorDepth
img = img[:, :, :3]
img = cv.resize(img, newSize, interpolation=cv.INTER_AREA)# zwraca ndarray
img = img / 255 #normalizacja
train_img.append(img)
categories_count.append(count)
X={}
X["values"] = np.array(train_img)
X["categories_name"] = categories_name
X["categories_count"] = categories_count
X["labels"]=labels
return X
def load_test_data(input_dir):
import numpy as np
import pandas as pd
import os
from skimage.io import imread
import cv2 as cv
from pathlib import Path
import random
from shutil import copyfile, rmtree
import json
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib
image_path = Path(input_dir)
labels_path = image_path.parents[0] / 'test_labels.json'
jsonString = labels_path.read_text()
objects = json.loads(jsonString)
categories_name = []
categories_count=[]
count = 0
c = objects[0]['value']
for e in objects:
if e['value'] != c:
categories_count.append(count)
c = e['value']
count = 1
else:
count += 1
if not e['value'] in categories_name:
categories_name.append(e['value'])
categories_count.append(count)
test_img = []
labels=[]
for e in objects:
p = image_path / e['filename']
img = imread(p)#zwraca ndarry postaci xSize x ySize x colorDepth
img = img[:, :, :3]
img = cv.resize(img, newSize, interpolation=cv.INTER_AREA)# zwraca ndarray
img = img / 255#normalizacja
test_img.append(img)
labels.append(e['value'])
X={}
X["values"] = np.array(test_img)
X["categories_name"] = categories_name
X["categories_count"] = categories_count
X["labels"]=labels
return Xdef create_tf_ds(X_train, y_train_enc, X_validate, y_validate_enc, X_test, y_test_enc):
import tensorflow as tf
train_ds = tf.data.Dataset.from_tensor_slices((X_train, y_train_enc))
validation_ds = tf.data.Dataset.from_tensor_slices((X_validate, y_validate_enc))
test_ds = tf.data.Dataset.from_tensor_slices((X_test, y_test_enc))
train_ds_size = tf.data.experimental.cardinality(train_ds).numpy()
test_ds_size = tf.data.experimental.cardinality(test_ds).numpy()
validation_ds_size = tf.data.experimental.cardinality(validation_ds).numpy()
print("Training data size:", train_ds_size)
print("Test data size:", test_ds_size)
print("Validation data size:", validation_ds_size)
train_ds = (train_ds
.shuffle(buffer_size=train_ds_size)
.batch(batch_size=32, drop_remainder=True))
test_ds = (test_ds
.shuffle(buffer_size=train_ds_size)
.batch(batch_size=32, drop_remainder=True))
validation_ds = (validation_ds
.shuffle(buffer_size=train_ds_size)
.batch(batch_size=32, drop_remainder=True))
return train_ds, test_ds, validation_dsdef get_run_logdir(root_logdir):
import os
import time
run_id = time.strftime("run_%Y_%m_%d-%H_%M_%S")
return os.path.join(root_logdir, run_id)def diagram_setup(model_name):
from tensorflow import keras
import os
root_logdir = os.path.join(os.curdir, f"logs\\\\fit\\\\\{model_name}\\\\")
run_logdir = get_run_logdir(root_logdir)
tensorboard_cb = keras.callbacks.TensorBoard(run_logdir)# Data load
data_train = load_train_data("./train_test_sw/train_sw")
values_train = data_train['values']
labels_train = data_train['labels']
data_test = load_test_data("./train_test_sw/test_sw")
X_test = data_test['values']
y_test = data_test['labels']from sklearn.model_selection import train_test_split
X_train, X_validate, y_train, y_validate = train_test_split(values_train, labels_train, test_size=0.2, random_state=42)from sklearn.preprocessing import LabelEncoder
class_le = LabelEncoder()
y_train_enc = class_le.fit_transform(y_train)
y_validate_enc = class_le.fit_transform(y_validate)
y_test_enc = class_le.fit_transform(y_test)train_ds, test_ds, validation_ds = create_tf_ds(X_train, y_train_enc, X_validate, y_validate_enc, X_test, y_test_enc)Training data size: 821 Test data size: 259 Validation data size: 206
VGG16
diagram_setup('vgg_sw')import keras,os
from keras.models import Sequential
from keras.layers import Dense, Conv2D, MaxPool2D , Flatten
from keras.preprocessing.image import ImageDataGenerator
import numpy as np
from keras.applications import VGG16
from keras.layers import Input, Lambda, Dense, Flatten
from keras.models import Model
from keras.preprocessing import image
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
import numpy as np
from glob import glob
import matplotlib.pyplot as plt
import ssl
ssl._create_default_https_context = ssl._create_unverified_context
# model = keras.models.Sequential([
# keras.layers.Conv2D(filters=64, kernel_size=(3,3), activation='relu', input_shape=(224,224,3), padding="same"),
# keras.layers.Conv2D(filters=64, kernel_size=(3,3), activation='relu', input_shape=(224,224,3), padding="same"),
# keras.layers.MaxPool2D(pool_size=(2,2), strides=(2,2)),
# keras.layers.Conv2D(filters=128, kernel_size=(3,3), padding="same", activation="relu"),
# keras.layers.Conv2D(filters=128, kernel_size=(3,3), padding="same", activation="relu"),
# keras.layers.MaxPool2D(pool_size=(2,2), strides=(2,2)),
# keras.layers.Conv2D(filters=256, kernel_size=(3,3), padding="same", activation="relu"),
# keras.layers.Conv2D(filters=256, kernel_size=(3,3), padding="same", activation="relu"),
# keras.layers.Conv2D(filters=256, kernel_size=(3,3), padding="same", activation="relu"),
# keras.layers.MaxPool2D(pool_size=(2,2), strides=(2,2)),
# keras.layers.Conv2D(filters=512, kernel_size=(3,3), padding="same", activation="relu"),
# keras.layers.Conv2D(filters=512, kernel_size=(3,3), padding="same", activation="relu"),
# keras.layers.Conv2D(filters=512, kernel_size=(3,3), padding="same", activation="relu"),
# keras.layers.MaxPool2D(pool_size=(2,2), strides=(2,2)),
# keras.layers.Conv2D(filters=512, kernel_size=(3,3), padding="same", activation="relu"),
# keras.layers.Conv2D(filters=512, kernel_size=(3,3), padding="same", activation="relu"),
# keras.layers.Conv2D(filters=512, kernel_size=(3,3), padding="same", activation="relu"),
# keras.layers.Flatten(),
# keras.layers.Dense(units = 4096, activation='relu'),
# keras.layers.Dense(units = 4096, activation='relu'),
# keras.layers.Dense(units = 5, activation='softmax')
# ])
# re-size all the images to this
IMAGE_SIZE = [224, 224]
# add preprocessing layer to the front of resnet
vgg2 = VGG16(input_shape=IMAGE_SIZE + [3], weights='imagenet', include_top=False)
# don't train existing weights
for layer in vgg2.layers:
layer.trainable = False
# useful for getting number of classes
classes = 5
# our layers - you can add more if you want
x = Flatten()(vgg2.output)
# x = Dense(1000, activation='relu')(x)
prediction = Dense(5, activation='softmax')(x)
# create a model object
model = Model(inputs=vgg2.input, outputs=prediction)
# view the structure of the model
model.summary()
# tell the model what cost and optimization method to use
model.compile(
loss='sparse_categorical_crossentropy',
optimizer='adam',
metrics=['accuracy']
)
#train_ds_vgg_sw, test_ds_vgg_sw, validation_ds_vgg_sw
# fit the model
vggr = model.fit_generator(
train_ds,
validation_data=validation_ds,
epochs=25,
steps_per_epoch=len(train_ds),
validation_steps=len(validation_ds)
)Model: "model_3"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_4 (InputLayer) [(None, 224, 224, 3)] 0
block1_conv1 (Conv2D) (None, 224, 224, 64) 1792
block1_conv2 (Conv2D) (None, 224, 224, 64) 36928
block1_pool (MaxPooling2D) (None, 112, 112, 64) 0
block2_conv1 (Conv2D) (None, 112, 112, 128) 73856
block2_conv2 (Conv2D) (None, 112, 112, 128) 147584
block2_pool (MaxPooling2D) (None, 56, 56, 128) 0
block3_conv1 (Conv2D) (None, 56, 56, 256) 295168
block3_conv2 (Conv2D) (None, 56, 56, 256) 590080
block3_conv3 (Conv2D) (None, 56, 56, 256) 590080
block3_pool (MaxPooling2D) (None, 28, 28, 256) 0
block4_conv1 (Conv2D) (None, 28, 28, 512) 1180160
block4_conv2 (Conv2D) (None, 28, 28, 512) 2359808
block4_conv3 (Conv2D) (None, 28, 28, 512) 2359808
block4_pool (MaxPooling2D) (None, 14, 14, 512) 0
block5_conv1 (Conv2D) (None, 14, 14, 512) 2359808
block5_conv2 (Conv2D) (None, 14, 14, 512) 2359808
block5_conv3 (Conv2D) (None, 14, 14, 512) 2359808
block5_pool (MaxPooling2D) (None, 7, 7, 512) 0
flatten_3 (Flatten) (None, 25088) 0
dense_3 (Dense) (None, 5) 125445
=================================================================
Total params: 14,840,133
Trainable params: 125,445
Non-trainable params: 14,714,688
_________________________________________________________________
Epoch 1/25
/var/folders/3r/c8tg1h051m18qhsdccdysrt40000gn/T/ipykernel_11345/3456911324.py:75: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators. vggr = model.fit_generator(
25/25 [==============================] - 117s 5s/step - loss: 1.4384 - accuracy: 0.4363 - val_loss: 0.8596 - val_accuracy: 0.6719 Epoch 2/25 25/25 [==============================] - 121s 5s/step - loss: 0.6040 - accuracy: 0.7975 - val_loss: 0.6615 - val_accuracy: 0.7552 Epoch 3/25 25/25 [==============================] - 126s 5s/step - loss: 0.3955 - accuracy: 0.9000 - val_loss: 0.5536 - val_accuracy: 0.7969 Epoch 4/25 25/25 [==============================] - 124s 5s/step - loss: 0.3278 - accuracy: 0.9237 - val_loss: 0.5154 - val_accuracy: 0.8438 Epoch 5/25 25/25 [==============================] - 124s 5s/step - loss: 0.2700 - accuracy: 0.9350 - val_loss: 0.5352 - val_accuracy: 0.7969 Epoch 6/25 25/25 [==============================] - 119s 5s/step - loss: 0.2109 - accuracy: 0.9538 - val_loss: 0.3983 - val_accuracy: 0.8854 Epoch 7/25 25/25 [==============================] - 117s 5s/step - loss: 0.1713 - accuracy: 0.9812 - val_loss: 0.3841 - val_accuracy: 0.8802 Epoch 8/25 25/25 [==============================] - 115s 5s/step - loss: 0.1519 - accuracy: 0.9850 - val_loss: 0.3871 - val_accuracy: 0.8854 Epoch 9/25 25/25 [==============================] - 117s 5s/step - loss: 0.1412 - accuracy: 0.9800 - val_loss: 0.4005 - val_accuracy: 0.8958 Epoch 10/25 25/25 [==============================] - 116s 5s/step - loss: 0.1176 - accuracy: 0.9900 - val_loss: 0.3657 - val_accuracy: 0.9062 Epoch 11/25 25/25 [==============================] - 116s 5s/step - loss: 0.1200 - accuracy: 0.9825 - val_loss: 0.3862 - val_accuracy: 0.8646 Epoch 12/25 25/25 [==============================] - 116s 5s/step - loss: 0.0958 - accuracy: 0.9912 - val_loss: 0.3412 - val_accuracy: 0.9010 Epoch 13/25 25/25 [==============================] - 113s 5s/step - loss: 0.0914 - accuracy: 0.9925 - val_loss: 0.3484 - val_accuracy: 0.8854 Epoch 14/25 25/25 [==============================] - 115s 5s/step - loss: 0.0799 - accuracy: 0.9950 - val_loss: 0.3406 - val_accuracy: 0.8906 Epoch 15/25 25/25 [==============================] - 118s 5s/step - loss: 0.0714 - accuracy: 0.9975 - val_loss: 0.3355 - val_accuracy: 0.8958 Epoch 16/25 25/25 [==============================] - 121s 5s/step - loss: 0.0728 - accuracy: 0.9950 - val_loss: 0.3384 - val_accuracy: 0.9062 Epoch 17/25 25/25 [==============================] - 120s 5s/step - loss: 0.0674 - accuracy: 0.9962 - val_loss: 0.3627 - val_accuracy: 0.8958 Epoch 18/25 25/25 [==============================] - 118s 5s/step - loss: 0.0580 - accuracy: 0.9962 - val_loss: 0.3231 - val_accuracy: 0.9115 Epoch 19/25 25/25 [==============================] - 118s 5s/step - loss: 0.0509 - accuracy: 0.9987 - val_loss: 0.3387 - val_accuracy: 0.8958 Epoch 20/25 25/25 [==============================] - 119s 5s/step - loss: 0.0492 - accuracy: 0.9987 - val_loss: 0.3076 - val_accuracy: 0.8906 Epoch 21/25 25/25 [==============================] - 1405s 58s/step - loss: 0.0458 - accuracy: 0.9987 - val_loss: 0.3350 - val_accuracy: 0.8854 Epoch 22/25 25/25 [==============================] - 1635s 68s/step - loss: 0.0458 - accuracy: 0.9975 - val_loss: 0.3148 - val_accuracy: 0.9062 Epoch 23/25 25/25 [==============================] - 103s 4s/step - loss: 0.0384 - accuracy: 1.0000 - val_loss: 0.3446 - val_accuracy: 0.8750 Epoch 24/25 25/25 [==============================] - 106s 4s/step - loss: 0.0387 - accuracy: 0.9987 - val_loss: 0.2885 - val_accuracy: 0.9062 Epoch 25/25 25/25 [==============================] - 109s 4s/step - loss: 0.0335 - accuracy: 1.0000 - val_loss: 0.2845 - val_accuracy: 0.8958
import matplotlib.pyplot as plt
plt.plot(vggr.history["accuracy"])
plt.plot(vggr.history['val_accuracy'])
plt.plot(vggr.history['loss'])
plt.plot(vggr.history['val_loss'])
plt.title("Model accuracy")
plt.ylabel("Value")
plt.xlabel("Epoch")
plt.legend(["Accuracy","Validation Accuracy","Loss","Validation Loss"])
plt.show()model.evaluate(test_ds)8/8 [==============================] - 29s 4s/step - loss: 0.3817 - accuracy: 0.8633
[0.38167834281921387, 0.86328125]
ResNet101V2
from keras.layers import Input, Lambda, Dense, Flatten
from keras.models import Model
from keras.preprocessing import image
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
import numpy as np
from glob import glob
import matplotlib.pyplot as plt
import ssl
ssl._create_default_https_context = ssl._create_unverified_context
from keras.applications import ResNet101V2
# re-size all the images to this
IMAGE_SIZE = [224, 224]
# add preprocessing layer to the front of resnet
resnet = ResNet101V2(input_shape=IMAGE_SIZE + [3], weights='imagenet', include_top=False)
# don't train existing weights
for layer in resnet.layers:
layer.trainable = False
# useful for getting number of classes
classes = 5
# our layers - you can add more if you want
x = Flatten()(resnet.output)
# x = Dense(1000, activation='relu')(x)
prediction = Dense(5, activation='softmax')(x)# create a model object
model = Model(inputs=resnet.input, outputs=prediction)
# view the structure of the model
model.summary()Model: "model_4"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_5 (InputLayer) [(None, 224, 224, 3 0 []
)]
conv1_pad (ZeroPadding2D) (None, 230, 230, 3) 0 ['input_5[0][0]']
conv1_conv (Conv2D) (None, 112, 112, 64 9472 ['conv1_pad[0][0]']
)
pool1_pad (ZeroPadding2D) (None, 114, 114, 64 0 ['conv1_conv[0][0]']
)
pool1_pool (MaxPooling2D) (None, 56, 56, 64) 0 ['pool1_pad[0][0]']
conv2_block1_preact_bn (BatchN (None, 56, 56, 64) 256 ['pool1_pool[0][0]']
ormalization)
conv2_block1_preact_relu (Acti (None, 56, 56, 64) 0 ['conv2_block1_preact_bn[0][0]']
vation)
conv2_block1_1_conv (Conv2D) (None, 56, 56, 64) 4096 ['conv2_block1_preact_relu[0][0]'
]
conv2_block1_1_bn (BatchNormal (None, 56, 56, 64) 256 ['conv2_block1_1_conv[0][0]']
ization)
conv2_block1_1_relu (Activatio (None, 56, 56, 64) 0 ['conv2_block1_1_bn[0][0]']
n)
conv2_block1_2_pad (ZeroPaddin (None, 58, 58, 64) 0 ['conv2_block1_1_relu[0][0]']
g2D)
conv2_block1_2_conv (Conv2D) (None, 56, 56, 64) 36864 ['conv2_block1_2_pad[0][0]']
conv2_block1_2_bn (BatchNormal (None, 56, 56, 64) 256 ['conv2_block1_2_conv[0][0]']
ization)
conv2_block1_2_relu (Activatio (None, 56, 56, 64) 0 ['conv2_block1_2_bn[0][0]']
n)
conv2_block1_0_conv (Conv2D) (None, 56, 56, 256) 16640 ['conv2_block1_preact_relu[0][0]'
]
conv2_block1_3_conv (Conv2D) (None, 56, 56, 256) 16640 ['conv2_block1_2_relu[0][0]']
conv2_block1_out (Add) (None, 56, 56, 256) 0 ['conv2_block1_0_conv[0][0]',
'conv2_block1_3_conv[0][0]']
conv2_block2_preact_bn (BatchN (None, 56, 56, 256) 1024 ['conv2_block1_out[0][0]']
ormalization)
conv2_block2_preact_relu (Acti (None, 56, 56, 256) 0 ['conv2_block2_preact_bn[0][0]']
vation)
conv2_block2_1_conv (Conv2D) (None, 56, 56, 64) 16384 ['conv2_block2_preact_relu[0][0]'
]
conv2_block2_1_bn (BatchNormal (None, 56, 56, 64) 256 ['conv2_block2_1_conv[0][0]']
ization)
conv2_block2_1_relu (Activatio (None, 56, 56, 64) 0 ['conv2_block2_1_bn[0][0]']
n)
conv2_block2_2_pad (ZeroPaddin (None, 58, 58, 64) 0 ['conv2_block2_1_relu[0][0]']
g2D)
conv2_block2_2_conv (Conv2D) (None, 56, 56, 64) 36864 ['conv2_block2_2_pad[0][0]']
conv2_block2_2_bn (BatchNormal (None, 56, 56, 64) 256 ['conv2_block2_2_conv[0][0]']
ization)
conv2_block2_2_relu (Activatio (None, 56, 56, 64) 0 ['conv2_block2_2_bn[0][0]']
n)
conv2_block2_3_conv (Conv2D) (None, 56, 56, 256) 16640 ['conv2_block2_2_relu[0][0]']
conv2_block2_out (Add) (None, 56, 56, 256) 0 ['conv2_block1_out[0][0]',
'conv2_block2_3_conv[0][0]']
conv2_block3_preact_bn (BatchN (None, 56, 56, 256) 1024 ['conv2_block2_out[0][0]']
ormalization)
conv2_block3_preact_relu (Acti (None, 56, 56, 256) 0 ['conv2_block3_preact_bn[0][0]']
vation)
conv2_block3_1_conv (Conv2D) (None, 56, 56, 64) 16384 ['conv2_block3_preact_relu[0][0]'
]
conv2_block3_1_bn (BatchNormal (None, 56, 56, 64) 256 ['conv2_block3_1_conv[0][0]']
ization)
conv2_block3_1_relu (Activatio (None, 56, 56, 64) 0 ['conv2_block3_1_bn[0][0]']
n)
conv2_block3_2_pad (ZeroPaddin (None, 58, 58, 64) 0 ['conv2_block3_1_relu[0][0]']
g2D)
conv2_block3_2_conv (Conv2D) (None, 28, 28, 64) 36864 ['conv2_block3_2_pad[0][0]']
conv2_block3_2_bn (BatchNormal (None, 28, 28, 64) 256 ['conv2_block3_2_conv[0][0]']
ization)
conv2_block3_2_relu (Activatio (None, 28, 28, 64) 0 ['conv2_block3_2_bn[0][0]']
n)
max_pooling2d_3 (MaxPooling2D) (None, 28, 28, 256) 0 ['conv2_block2_out[0][0]']
conv2_block3_3_conv (Conv2D) (None, 28, 28, 256) 16640 ['conv2_block3_2_relu[0][0]']
conv2_block3_out (Add) (None, 28, 28, 256) 0 ['max_pooling2d_3[0][0]',
'conv2_block3_3_conv[0][0]']
conv3_block1_preact_bn (BatchN (None, 28, 28, 256) 1024 ['conv2_block3_out[0][0]']
ormalization)
conv3_block1_preact_relu (Acti (None, 28, 28, 256) 0 ['conv3_block1_preact_bn[0][0]']
vation)
conv3_block1_1_conv (Conv2D) (None, 28, 28, 128) 32768 ['conv3_block1_preact_relu[0][0]'
]
conv3_block1_1_bn (BatchNormal (None, 28, 28, 128) 512 ['conv3_block1_1_conv[0][0]']
ization)
conv3_block1_1_relu (Activatio (None, 28, 28, 128) 0 ['conv3_block1_1_bn[0][0]']
n)
conv3_block1_2_pad (ZeroPaddin (None, 30, 30, 128) 0 ['conv3_block1_1_relu[0][0]']
g2D)
conv3_block1_2_conv (Conv2D) (None, 28, 28, 128) 147456 ['conv3_block1_2_pad[0][0]']
conv3_block1_2_bn (BatchNormal (None, 28, 28, 128) 512 ['conv3_block1_2_conv[0][0]']
ization)
conv3_block1_2_relu (Activatio (None, 28, 28, 128) 0 ['conv3_block1_2_bn[0][0]']
n)
conv3_block1_0_conv (Conv2D) (None, 28, 28, 512) 131584 ['conv3_block1_preact_relu[0][0]'
]
conv3_block1_3_conv (Conv2D) (None, 28, 28, 512) 66048 ['conv3_block1_2_relu[0][0]']
conv3_block1_out (Add) (None, 28, 28, 512) 0 ['conv3_block1_0_conv[0][0]',
'conv3_block1_3_conv[0][0]']
conv3_block2_preact_bn (BatchN (None, 28, 28, 512) 2048 ['conv3_block1_out[0][0]']
ormalization)
conv3_block2_preact_relu (Acti (None, 28, 28, 512) 0 ['conv3_block2_preact_bn[0][0]']
vation)
conv3_block2_1_conv (Conv2D) (None, 28, 28, 128) 65536 ['conv3_block2_preact_relu[0][0]'
]
conv3_block2_1_bn (BatchNormal (None, 28, 28, 128) 512 ['conv3_block2_1_conv[0][0]']
ization)
conv3_block2_1_relu (Activatio (None, 28, 28, 128) 0 ['conv3_block2_1_bn[0][0]']
n)
conv3_block2_2_pad (ZeroPaddin (None, 30, 30, 128) 0 ['conv3_block2_1_relu[0][0]']
g2D)
conv3_block2_2_conv (Conv2D) (None, 28, 28, 128) 147456 ['conv3_block2_2_pad[0][0]']
conv3_block2_2_bn (BatchNormal (None, 28, 28, 128) 512 ['conv3_block2_2_conv[0][0]']
ization)
conv3_block2_2_relu (Activatio (None, 28, 28, 128) 0 ['conv3_block2_2_bn[0][0]']
n)
conv3_block2_3_conv (Conv2D) (None, 28, 28, 512) 66048 ['conv3_block2_2_relu[0][0]']
conv3_block2_out (Add) (None, 28, 28, 512) 0 ['conv3_block1_out[0][0]',
'conv3_block2_3_conv[0][0]']
conv3_block3_preact_bn (BatchN (None, 28, 28, 512) 2048 ['conv3_block2_out[0][0]']
ormalization)
conv3_block3_preact_relu (Acti (None, 28, 28, 512) 0 ['conv3_block3_preact_bn[0][0]']
vation)
conv3_block3_1_conv (Conv2D) (None, 28, 28, 128) 65536 ['conv3_block3_preact_relu[0][0]'
]
conv3_block3_1_bn (BatchNormal (None, 28, 28, 128) 512 ['conv3_block3_1_conv[0][0]']
ization)
conv3_block3_1_relu (Activatio (None, 28, 28, 128) 0 ['conv3_block3_1_bn[0][0]']
n)
conv3_block3_2_pad (ZeroPaddin (None, 30, 30, 128) 0 ['conv3_block3_1_relu[0][0]']
g2D)
conv3_block3_2_conv (Conv2D) (None, 28, 28, 128) 147456 ['conv3_block3_2_pad[0][0]']
conv3_block3_2_bn (BatchNormal (None, 28, 28, 128) 512 ['conv3_block3_2_conv[0][0]']
ization)
conv3_block3_2_relu (Activatio (None, 28, 28, 128) 0 ['conv3_block3_2_bn[0][0]']
n)
conv3_block3_3_conv (Conv2D) (None, 28, 28, 512) 66048 ['conv3_block3_2_relu[0][0]']
conv3_block3_out (Add) (None, 28, 28, 512) 0 ['conv3_block2_out[0][0]',
'conv3_block3_3_conv[0][0]']
conv3_block4_preact_bn (BatchN (None, 28, 28, 512) 2048 ['conv3_block3_out[0][0]']
ormalization)
conv3_block4_preact_relu (Acti (None, 28, 28, 512) 0 ['conv3_block4_preact_bn[0][0]']
vation)
conv3_block4_1_conv (Conv2D) (None, 28, 28, 128) 65536 ['conv3_block4_preact_relu[0][0]'
]
conv3_block4_1_bn (BatchNormal (None, 28, 28, 128) 512 ['conv3_block4_1_conv[0][0]']
ization)
conv3_block4_1_relu (Activatio (None, 28, 28, 128) 0 ['conv3_block4_1_bn[0][0]']
n)
conv3_block4_2_pad (ZeroPaddin (None, 30, 30, 128) 0 ['conv3_block4_1_relu[0][0]']
g2D)
conv3_block4_2_conv (Conv2D) (None, 14, 14, 128) 147456 ['conv3_block4_2_pad[0][0]']
conv3_block4_2_bn (BatchNormal (None, 14, 14, 128) 512 ['conv3_block4_2_conv[0][0]']
ization)
conv3_block4_2_relu (Activatio (None, 14, 14, 128) 0 ['conv3_block4_2_bn[0][0]']
n)
max_pooling2d_4 (MaxPooling2D) (None, 14, 14, 512) 0 ['conv3_block3_out[0][0]']
conv3_block4_3_conv (Conv2D) (None, 14, 14, 512) 66048 ['conv3_block4_2_relu[0][0]']
conv3_block4_out (Add) (None, 14, 14, 512) 0 ['max_pooling2d_4[0][0]',
'conv3_block4_3_conv[0][0]']
conv4_block1_preact_bn (BatchN (None, 14, 14, 512) 2048 ['conv3_block4_out[0][0]']
ormalization)
conv4_block1_preact_relu (Acti (None, 14, 14, 512) 0 ['conv4_block1_preact_bn[0][0]']
vation)
conv4_block1_1_conv (Conv2D) (None, 14, 14, 256) 131072 ['conv4_block1_preact_relu[0][0]'
]
conv4_block1_1_bn (BatchNormal (None, 14, 14, 256) 1024 ['conv4_block1_1_conv[0][0]']
ization)
conv4_block1_1_relu (Activatio (None, 14, 14, 256) 0 ['conv4_block1_1_bn[0][0]']
n)
conv4_block1_2_pad (ZeroPaddin (None, 16, 16, 256) 0 ['conv4_block1_1_relu[0][0]']
g2D)
conv4_block1_2_conv (Conv2D) (None, 14, 14, 256) 589824 ['conv4_block1_2_pad[0][0]']
conv4_block1_2_bn (BatchNormal (None, 14, 14, 256) 1024 ['conv4_block1_2_conv[0][0]']
ization)
conv4_block1_2_relu (Activatio (None, 14, 14, 256) 0 ['conv4_block1_2_bn[0][0]']
n)
conv4_block1_0_conv (Conv2D) (None, 14, 14, 1024 525312 ['conv4_block1_preact_relu[0][0]'
) ]
conv4_block1_3_conv (Conv2D) (None, 14, 14, 1024 263168 ['conv4_block1_2_relu[0][0]']
)
conv4_block1_out (Add) (None, 14, 14, 1024 0 ['conv4_block1_0_conv[0][0]',
) 'conv4_block1_3_conv[0][0]']
conv4_block2_preact_bn (BatchN (None, 14, 14, 1024 4096 ['conv4_block1_out[0][0]']
ormalization) )
conv4_block2_preact_relu (Acti (None, 14, 14, 1024 0 ['conv4_block2_preact_bn[0][0]']
vation) )
conv4_block2_1_conv (Conv2D) (None, 14, 14, 256) 262144 ['conv4_block2_preact_relu[0][0]'
]
conv4_block2_1_bn (BatchNormal (None, 14, 14, 256) 1024 ['conv4_block2_1_conv[0][0]']
ization)
conv4_block2_1_relu (Activatio (None, 14, 14, 256) 0 ['conv4_block2_1_bn[0][0]']
n)
conv4_block2_2_pad (ZeroPaddin (None, 16, 16, 256) 0 ['conv4_block2_1_relu[0][0]']
g2D)
conv4_block2_2_conv (Conv2D) (None, 14, 14, 256) 589824 ['conv4_block2_2_pad[0][0]']
conv4_block2_2_bn (BatchNormal (None, 14, 14, 256) 1024 ['conv4_block2_2_conv[0][0]']
ization)
conv4_block2_2_relu (Activatio (None, 14, 14, 256) 0 ['conv4_block2_2_bn[0][0]']
n)
conv4_block2_3_conv (Conv2D) (None, 14, 14, 1024 263168 ['conv4_block2_2_relu[0][0]']
)
conv4_block2_out (Add) (None, 14, 14, 1024 0 ['conv4_block1_out[0][0]',
) 'conv4_block2_3_conv[0][0]']
conv4_block3_preact_bn (BatchN (None, 14, 14, 1024 4096 ['conv4_block2_out[0][0]']
ormalization) )
conv4_block3_preact_relu (Acti (None, 14, 14, 1024 0 ['conv4_block3_preact_bn[0][0]']
vation) )
conv4_block3_1_conv (Conv2D) (None, 14, 14, 256) 262144 ['conv4_block3_preact_relu[0][0]'
]
conv4_block3_1_bn (BatchNormal (None, 14, 14, 256) 1024 ['conv4_block3_1_conv[0][0]']
ization)
conv4_block3_1_relu (Activatio (None, 14, 14, 256) 0 ['conv4_block3_1_bn[0][0]']
n)
conv4_block3_2_pad (ZeroPaddin (None, 16, 16, 256) 0 ['conv4_block3_1_relu[0][0]']
g2D)
conv4_block3_2_conv (Conv2D) (None, 14, 14, 256) 589824 ['conv4_block3_2_pad[0][0]']
conv4_block3_2_bn (BatchNormal (None, 14, 14, 256) 1024 ['conv4_block3_2_conv[0][0]']
ization)
conv4_block3_2_relu (Activatio (None, 14, 14, 256) 0 ['conv4_block3_2_bn[0][0]']
n)
conv4_block3_3_conv (Conv2D) (None, 14, 14, 1024 263168 ['conv4_block3_2_relu[0][0]']
)
conv4_block3_out (Add) (None, 14, 14, 1024 0 ['conv4_block2_out[0][0]',
) 'conv4_block3_3_conv[0][0]']
conv4_block4_preact_bn (BatchN (None, 14, 14, 1024 4096 ['conv4_block3_out[0][0]']
ormalization) )
conv4_block4_preact_relu (Acti (None, 14, 14, 1024 0 ['conv4_block4_preact_bn[0][0]']
vation) )
conv4_block4_1_conv (Conv2D) (None, 14, 14, 256) 262144 ['conv4_block4_preact_relu[0][0]'
]
conv4_block4_1_bn (BatchNormal (None, 14, 14, 256) 1024 ['conv4_block4_1_conv[0][0]']
ization)
conv4_block4_1_relu (Activatio (None, 14, 14, 256) 0 ['conv4_block4_1_bn[0][0]']
n)
conv4_block4_2_pad (ZeroPaddin (None, 16, 16, 256) 0 ['conv4_block4_1_relu[0][0]']
g2D)
conv4_block4_2_conv (Conv2D) (None, 14, 14, 256) 589824 ['conv4_block4_2_pad[0][0]']
conv4_block4_2_bn (BatchNormal (None, 14, 14, 256) 1024 ['conv4_block4_2_conv[0][0]']
ization)
conv4_block4_2_relu (Activatio (None, 14, 14, 256) 0 ['conv4_block4_2_bn[0][0]']
n)
conv4_block4_3_conv (Conv2D) (None, 14, 14, 1024 263168 ['conv4_block4_2_relu[0][0]']
)
conv4_block4_out (Add) (None, 14, 14, 1024 0 ['conv4_block3_out[0][0]',
) 'conv4_block4_3_conv[0][0]']
conv4_block5_preact_bn (BatchN (None, 14, 14, 1024 4096 ['conv4_block4_out[0][0]']
ormalization) )
conv4_block5_preact_relu (Acti (None, 14, 14, 1024 0 ['conv4_block5_preact_bn[0][0]']
vation) )
conv4_block5_1_conv (Conv2D) (None, 14, 14, 256) 262144 ['conv4_block5_preact_relu[0][0]'
]
conv4_block5_1_bn (BatchNormal (None, 14, 14, 256) 1024 ['conv4_block5_1_conv[0][0]']
ization)
conv4_block5_1_relu (Activatio (None, 14, 14, 256) 0 ['conv4_block5_1_bn[0][0]']
n)
conv4_block5_2_pad (ZeroPaddin (None, 16, 16, 256) 0 ['conv4_block5_1_relu[0][0]']
g2D)
conv4_block5_2_conv (Conv2D) (None, 14, 14, 256) 589824 ['conv4_block5_2_pad[0][0]']
conv4_block5_2_bn (BatchNormal (None, 14, 14, 256) 1024 ['conv4_block5_2_conv[0][0]']
ization)
conv4_block5_2_relu (Activatio (None, 14, 14, 256) 0 ['conv4_block5_2_bn[0][0]']
n)
conv4_block5_3_conv (Conv2D) (None, 14, 14, 1024 263168 ['conv4_block5_2_relu[0][0]']
)
conv4_block5_out (Add) (None, 14, 14, 1024 0 ['conv4_block4_out[0][0]',
) 'conv4_block5_3_conv[0][0]']
conv4_block6_preact_bn (BatchN (None, 14, 14, 1024 4096 ['conv4_block5_out[0][0]']
ormalization) )
conv4_block6_preact_relu (Acti (None, 14, 14, 1024 0 ['conv4_block6_preact_bn[0][0]']
vation) )
conv4_block6_1_conv (Conv2D) (None, 14, 14, 256) 262144 ['conv4_block6_preact_relu[0][0]'
]
conv4_block6_1_bn (BatchNormal (None, 14, 14, 256) 1024 ['conv4_block6_1_conv[0][0]']
ization)
conv4_block6_1_relu (Activatio (None, 14, 14, 256) 0 ['conv4_block6_1_bn[0][0]']
n)
conv4_block6_2_pad (ZeroPaddin (None, 16, 16, 256) 0 ['conv4_block6_1_relu[0][0]']
g2D)
conv4_block6_2_conv (Conv2D) (None, 14, 14, 256) 589824 ['conv4_block6_2_pad[0][0]']
conv4_block6_2_bn (BatchNormal (None, 14, 14, 256) 1024 ['conv4_block6_2_conv[0][0]']
ization)
conv4_block6_2_relu (Activatio (None, 14, 14, 256) 0 ['conv4_block6_2_bn[0][0]']
n)
conv4_block6_3_conv (Conv2D) (None, 14, 14, 1024 263168 ['conv4_block6_2_relu[0][0]']
)
conv4_block6_out (Add) (None, 14, 14, 1024 0 ['conv4_block5_out[0][0]',
) 'conv4_block6_3_conv[0][0]']
conv4_block7_preact_bn (BatchN (None, 14, 14, 1024 4096 ['conv4_block6_out[0][0]']
ormalization) )
conv4_block7_preact_relu (Acti (None, 14, 14, 1024 0 ['conv4_block7_preact_bn[0][0]']
vation) )
conv4_block7_1_conv (Conv2D) (None, 14, 14, 256) 262144 ['conv4_block7_preact_relu[0][0]'
]
conv4_block7_1_bn (BatchNormal (None, 14, 14, 256) 1024 ['conv4_block7_1_conv[0][0]']
ization)
conv4_block7_1_relu (Activatio (None, 14, 14, 256) 0 ['conv4_block7_1_bn[0][0]']
n)
conv4_block7_2_pad (ZeroPaddin (None, 16, 16, 256) 0 ['conv4_block7_1_relu[0][0]']
g2D)
conv4_block7_2_conv (Conv2D) (None, 14, 14, 256) 589824 ['conv4_block7_2_pad[0][0]']
conv4_block7_2_bn (BatchNormal (None, 14, 14, 256) 1024 ['conv4_block7_2_conv[0][0]']
ization)
conv4_block7_2_relu (Activatio (None, 14, 14, 256) 0 ['conv4_block7_2_bn[0][0]']
n)
conv4_block7_3_conv (Conv2D) (None, 14, 14, 1024 263168 ['conv4_block7_2_relu[0][0]']
)
conv4_block7_out (Add) (None, 14, 14, 1024 0 ['conv4_block6_out[0][0]',
) 'conv4_block7_3_conv[0][0]']
conv4_block8_preact_bn (BatchN (None, 14, 14, 1024 4096 ['conv4_block7_out[0][0]']
ormalization) )
conv4_block8_preact_relu (Acti (None, 14, 14, 1024 0 ['conv4_block8_preact_bn[0][0]']
vation) )
conv4_block8_1_conv (Conv2D) (None, 14, 14, 256) 262144 ['conv4_block8_preact_relu[0][0]'
]
conv4_block8_1_bn (BatchNormal (None, 14, 14, 256) 1024 ['conv4_block8_1_conv[0][0]']
ization)
conv4_block8_1_relu (Activatio (None, 14, 14, 256) 0 ['conv4_block8_1_bn[0][0]']
n)
conv4_block8_2_pad (ZeroPaddin (None, 16, 16, 256) 0 ['conv4_block8_1_relu[0][0]']
g2D)
conv4_block8_2_conv (Conv2D) (None, 14, 14, 256) 589824 ['conv4_block8_2_pad[0][0]']
conv4_block8_2_bn (BatchNormal (None, 14, 14, 256) 1024 ['conv4_block8_2_conv[0][0]']
ization)
conv4_block8_2_relu (Activatio (None, 14, 14, 256) 0 ['conv4_block8_2_bn[0][0]']
n)
conv4_block8_3_conv (Conv2D) (None, 14, 14, 1024 263168 ['conv4_block8_2_relu[0][0]']
)
conv4_block8_out (Add) (None, 14, 14, 1024 0 ['conv4_block7_out[0][0]',
) 'conv4_block8_3_conv[0][0]']
conv4_block9_preact_bn (BatchN (None, 14, 14, 1024 4096 ['conv4_block8_out[0][0]']
ormalization) )
conv4_block9_preact_relu (Acti (None, 14, 14, 1024 0 ['conv4_block9_preact_bn[0][0]']
vation) )
conv4_block9_1_conv (Conv2D) (None, 14, 14, 256) 262144 ['conv4_block9_preact_relu[0][0]'
]
conv4_block9_1_bn (BatchNormal (None, 14, 14, 256) 1024 ['conv4_block9_1_conv[0][0]']
ization)
conv4_block9_1_relu (Activatio (None, 14, 14, 256) 0 ['conv4_block9_1_bn[0][0]']
n)
conv4_block9_2_pad (ZeroPaddin (None, 16, 16, 256) 0 ['conv4_block9_1_relu[0][0]']
g2D)
conv4_block9_2_conv (Conv2D) (None, 14, 14, 256) 589824 ['conv4_block9_2_pad[0][0]']
conv4_block9_2_bn (BatchNormal (None, 14, 14, 256) 1024 ['conv4_block9_2_conv[0][0]']
ization)
conv4_block9_2_relu (Activatio (None, 14, 14, 256) 0 ['conv4_block9_2_bn[0][0]']
n)
conv4_block9_3_conv (Conv2D) (None, 14, 14, 1024 263168 ['conv4_block9_2_relu[0][0]']
)
conv4_block9_out (Add) (None, 14, 14, 1024 0 ['conv4_block8_out[0][0]',
) 'conv4_block9_3_conv[0][0]']
conv4_block10_preact_bn (Batch (None, 14, 14, 1024 4096 ['conv4_block9_out[0][0]']
Normalization) )
conv4_block10_preact_relu (Act (None, 14, 14, 1024 0 ['conv4_block10_preact_bn[0][0]']
ivation) )
conv4_block10_1_conv (Conv2D) (None, 14, 14, 256) 262144 ['conv4_block10_preact_relu[0][0]
']
conv4_block10_1_bn (BatchNorma (None, 14, 14, 256) 1024 ['conv4_block10_1_conv[0][0]']
lization)
conv4_block10_1_relu (Activati (None, 14, 14, 256) 0 ['conv4_block10_1_bn[0][0]']
on)
conv4_block10_2_pad (ZeroPaddi (None, 16, 16, 256) 0 ['conv4_block10_1_relu[0][0]']
ng2D)
conv4_block10_2_conv (Conv2D) (None, 14, 14, 256) 589824 ['conv4_block10_2_pad[0][0]']
conv4_block10_2_bn (BatchNorma (None, 14, 14, 256) 1024 ['conv4_block10_2_conv[0][0]']
lization)
conv4_block10_2_relu (Activati (None, 14, 14, 256) 0 ['conv4_block10_2_bn[0][0]']
on)
conv4_block10_3_conv (Conv2D) (None, 14, 14, 1024 263168 ['conv4_block10_2_relu[0][0]']
)
conv4_block10_out (Add) (None, 14, 14, 1024 0 ['conv4_block9_out[0][0]',
) 'conv4_block10_3_conv[0][0]']
conv4_block11_preact_bn (Batch (None, 14, 14, 1024 4096 ['conv4_block10_out[0][0]']
Normalization) )
conv4_block11_preact_relu (Act (None, 14, 14, 1024 0 ['conv4_block11_preact_bn[0][0]']
ivation) )
conv4_block11_1_conv (Conv2D) (None, 14, 14, 256) 262144 ['conv4_block11_preact_relu[0][0]
']
conv4_block11_1_bn (BatchNorma (None, 14, 14, 256) 1024 ['conv4_block11_1_conv[0][0]']
lization)
conv4_block11_1_relu (Activati (None, 14, 14, 256) 0 ['conv4_block11_1_bn[0][0]']
on)
conv4_block11_2_pad (ZeroPaddi (None, 16, 16, 256) 0 ['conv4_block11_1_relu[0][0]']
ng2D)
conv4_block11_2_conv (Conv2D) (None, 14, 14, 256) 589824 ['conv4_block11_2_pad[0][0]']
conv4_block11_2_bn (BatchNorma (None, 14, 14, 256) 1024 ['conv4_block11_2_conv[0][0]']
lization)
conv4_block11_2_relu (Activati (None, 14, 14, 256) 0 ['conv4_block11_2_bn[0][0]']
on)
conv4_block11_3_conv (Conv2D) (None, 14, 14, 1024 263168 ['conv4_block11_2_relu[0][0]']
)
conv4_block11_out (Add) (None, 14, 14, 1024 0 ['conv4_block10_out[0][0]',
) 'conv4_block11_3_conv[0][0]']
conv4_block12_preact_bn (Batch (None, 14, 14, 1024 4096 ['conv4_block11_out[0][0]']
Normalization) )
conv4_block12_preact_relu (Act (None, 14, 14, 1024 0 ['conv4_block12_preact_bn[0][0]']
ivation) )
conv4_block12_1_conv (Conv2D) (None, 14, 14, 256) 262144 ['conv4_block12_preact_relu[0][0]
']
conv4_block12_1_bn (BatchNorma (None, 14, 14, 256) 1024 ['conv4_block12_1_conv[0][0]']
lization)
conv4_block12_1_relu (Activati (None, 14, 14, 256) 0 ['conv4_block12_1_bn[0][0]']
on)
conv4_block12_2_pad (ZeroPaddi (None, 16, 16, 256) 0 ['conv4_block12_1_relu[0][0]']
ng2D)
conv4_block12_2_conv (Conv2D) (None, 14, 14, 256) 589824 ['conv4_block12_2_pad[0][0]']
conv4_block12_2_bn (BatchNorma (None, 14, 14, 256) 1024 ['conv4_block12_2_conv[0][0]']
lization)
conv4_block12_2_relu (Activati (None, 14, 14, 256) 0 ['conv4_block12_2_bn[0][0]']
on)
conv4_block12_3_conv (Conv2D) (None, 14, 14, 1024 263168 ['conv4_block12_2_relu[0][0]']
)
conv4_block12_out (Add) (None, 14, 14, 1024 0 ['conv4_block11_out[0][0]',
) 'conv4_block12_3_conv[0][0]']
conv4_block13_preact_bn (Batch (None, 14, 14, 1024 4096 ['conv4_block12_out[0][0]']
Normalization) )
conv4_block13_preact_relu (Act (None, 14, 14, 1024 0 ['conv4_block13_preact_bn[0][0]']
ivation) )
conv4_block13_1_conv (Conv2D) (None, 14, 14, 256) 262144 ['conv4_block13_preact_relu[0][0]
']
conv4_block13_1_bn (BatchNorma (None, 14, 14, 256) 1024 ['conv4_block13_1_conv[0][0]']
lization)
conv4_block13_1_relu (Activati (None, 14, 14, 256) 0 ['conv4_block13_1_bn[0][0]']
on)
conv4_block13_2_pad (ZeroPaddi (None, 16, 16, 256) 0 ['conv4_block13_1_relu[0][0]']
ng2D)
conv4_block13_2_conv (Conv2D) (None, 14, 14, 256) 589824 ['conv4_block13_2_pad[0][0]']
conv4_block13_2_bn (BatchNorma (None, 14, 14, 256) 1024 ['conv4_block13_2_conv[0][0]']
lization)
conv4_block13_2_relu (Activati (None, 14, 14, 256) 0 ['conv4_block13_2_bn[0][0]']
on)
conv4_block13_3_conv (Conv2D) (None, 14, 14, 1024 263168 ['conv4_block13_2_relu[0][0]']
)
conv4_block13_out (Add) (None, 14, 14, 1024 0 ['conv4_block12_out[0][0]',
) 'conv4_block13_3_conv[0][0]']
conv4_block14_preact_bn (Batch (None, 14, 14, 1024 4096 ['conv4_block13_out[0][0]']
Normalization) )
conv4_block14_preact_relu (Act (None, 14, 14, 1024 0 ['conv4_block14_preact_bn[0][0]']
ivation) )
conv4_block14_1_conv (Conv2D) (None, 14, 14, 256) 262144 ['conv4_block14_preact_relu[0][0]
']
conv4_block14_1_bn (BatchNorma (None, 14, 14, 256) 1024 ['conv4_block14_1_conv[0][0]']
lization)
conv4_block14_1_relu (Activati (None, 14, 14, 256) 0 ['conv4_block14_1_bn[0][0]']
on)
conv4_block14_2_pad (ZeroPaddi (None, 16, 16, 256) 0 ['conv4_block14_1_relu[0][0]']
ng2D)
conv4_block14_2_conv (Conv2D) (None, 14, 14, 256) 589824 ['conv4_block14_2_pad[0][0]']
conv4_block14_2_bn (BatchNorma (None, 14, 14, 256) 1024 ['conv4_block14_2_conv[0][0]']
lization)
conv4_block14_2_relu (Activati (None, 14, 14, 256) 0 ['conv4_block14_2_bn[0][0]']
on)
conv4_block14_3_conv (Conv2D) (None, 14, 14, 1024 263168 ['conv4_block14_2_relu[0][0]']
)
conv4_block14_out (Add) (None, 14, 14, 1024 0 ['conv4_block13_out[0][0]',
) 'conv4_block14_3_conv[0][0]']
conv4_block15_preact_bn (Batch (None, 14, 14, 1024 4096 ['conv4_block14_out[0][0]']
Normalization) )
conv4_block15_preact_relu (Act (None, 14, 14, 1024 0 ['conv4_block15_preact_bn[0][0]']
ivation) )
conv4_block15_1_conv (Conv2D) (None, 14, 14, 256) 262144 ['conv4_block15_preact_relu[0][0]
']
conv4_block15_1_bn (BatchNorma (None, 14, 14, 256) 1024 ['conv4_block15_1_conv[0][0]']
lization)
conv4_block15_1_relu (Activati (None, 14, 14, 256) 0 ['conv4_block15_1_bn[0][0]']
on)
conv4_block15_2_pad (ZeroPaddi (None, 16, 16, 256) 0 ['conv4_block15_1_relu[0][0]']
ng2D)
conv4_block15_2_conv (Conv2D) (None, 14, 14, 256) 589824 ['conv4_block15_2_pad[0][0]']
conv4_block15_2_bn (BatchNorma (None, 14, 14, 256) 1024 ['conv4_block15_2_conv[0][0]']
lization)
conv4_block15_2_relu (Activati (None, 14, 14, 256) 0 ['conv4_block15_2_bn[0][0]']
on)
conv4_block15_3_conv (Conv2D) (None, 14, 14, 1024 263168 ['conv4_block15_2_relu[0][0]']
)
conv4_block15_out (Add) (None, 14, 14, 1024 0 ['conv4_block14_out[0][0]',
) 'conv4_block15_3_conv[0][0]']
conv4_block16_preact_bn (Batch (None, 14, 14, 1024 4096 ['conv4_block15_out[0][0]']
Normalization) )
conv4_block16_preact_relu (Act (None, 14, 14, 1024 0 ['conv4_block16_preact_bn[0][0]']
ivation) )
conv4_block16_1_conv (Conv2D) (None, 14, 14, 256) 262144 ['conv4_block16_preact_relu[0][0]
']
conv4_block16_1_bn (BatchNorma (None, 14, 14, 256) 1024 ['conv4_block16_1_conv[0][0]']
lization)
conv4_block16_1_relu (Activati (None, 14, 14, 256) 0 ['conv4_block16_1_bn[0][0]']
on)
conv4_block16_2_pad (ZeroPaddi (None, 16, 16, 256) 0 ['conv4_block16_1_relu[0][0]']
ng2D)
conv4_block16_2_conv (Conv2D) (None, 14, 14, 256) 589824 ['conv4_block16_2_pad[0][0]']
conv4_block16_2_bn (BatchNorma (None, 14, 14, 256) 1024 ['conv4_block16_2_conv[0][0]']
lization)
conv4_block16_2_relu (Activati (None, 14, 14, 256) 0 ['conv4_block16_2_bn[0][0]']
on)
conv4_block16_3_conv (Conv2D) (None, 14, 14, 1024 263168 ['conv4_block16_2_relu[0][0]']
)
conv4_block16_out (Add) (None, 14, 14, 1024 0 ['conv4_block15_out[0][0]',
) 'conv4_block16_3_conv[0][0]']
conv4_block17_preact_bn (Batch (None, 14, 14, 1024 4096 ['conv4_block16_out[0][0]']
Normalization) )
conv4_block17_preact_relu (Act (None, 14, 14, 1024 0 ['conv4_block17_preact_bn[0][0]']
ivation) )
conv4_block17_1_conv (Conv2D) (None, 14, 14, 256) 262144 ['conv4_block17_preact_relu[0][0]
']
conv4_block17_1_bn (BatchNorma (None, 14, 14, 256) 1024 ['conv4_block17_1_conv[0][0]']
lization)
conv4_block17_1_relu (Activati (None, 14, 14, 256) 0 ['conv4_block17_1_bn[0][0]']
on)
conv4_block17_2_pad (ZeroPaddi (None, 16, 16, 256) 0 ['conv4_block17_1_relu[0][0]']
ng2D)
conv4_block17_2_conv (Conv2D) (None, 14, 14, 256) 589824 ['conv4_block17_2_pad[0][0]']
conv4_block17_2_bn (BatchNorma (None, 14, 14, 256) 1024 ['conv4_block17_2_conv[0][0]']
lization)
conv4_block17_2_relu (Activati (None, 14, 14, 256) 0 ['conv4_block17_2_bn[0][0]']
on)
conv4_block17_3_conv (Conv2D) (None, 14, 14, 1024 263168 ['conv4_block17_2_relu[0][0]']
)
conv4_block17_out (Add) (None, 14, 14, 1024 0 ['conv4_block16_out[0][0]',
) 'conv4_block17_3_conv[0][0]']
conv4_block18_preact_bn (Batch (None, 14, 14, 1024 4096 ['conv4_block17_out[0][0]']
Normalization) )
conv4_block18_preact_relu (Act (None, 14, 14, 1024 0 ['conv4_block18_preact_bn[0][0]']
ivation) )
conv4_block18_1_conv (Conv2D) (None, 14, 14, 256) 262144 ['conv4_block18_preact_relu[0][0]
']
conv4_block18_1_bn (BatchNorma (None, 14, 14, 256) 1024 ['conv4_block18_1_conv[0][0]']
lization)
conv4_block18_1_relu (Activati (None, 14, 14, 256) 0 ['conv4_block18_1_bn[0][0]']
on)
conv4_block18_2_pad (ZeroPaddi (None, 16, 16, 256) 0 ['conv4_block18_1_relu[0][0]']
ng2D)
conv4_block18_2_conv (Conv2D) (None, 14, 14, 256) 589824 ['conv4_block18_2_pad[0][0]']
conv4_block18_2_bn (BatchNorma (None, 14, 14, 256) 1024 ['conv4_block18_2_conv[0][0]']
lization)
conv4_block18_2_relu (Activati (None, 14, 14, 256) 0 ['conv4_block18_2_bn[0][0]']
on)
conv4_block18_3_conv (Conv2D) (None, 14, 14, 1024 263168 ['conv4_block18_2_relu[0][0]']
)
conv4_block18_out (Add) (None, 14, 14, 1024 0 ['conv4_block17_out[0][0]',
) 'conv4_block18_3_conv[0][0]']
conv4_block19_preact_bn (Batch (None, 14, 14, 1024 4096 ['conv4_block18_out[0][0]']
Normalization) )
conv4_block19_preact_relu (Act (None, 14, 14, 1024 0 ['conv4_block19_preact_bn[0][0]']
ivation) )
conv4_block19_1_conv (Conv2D) (None, 14, 14, 256) 262144 ['conv4_block19_preact_relu[0][0]
']
conv4_block19_1_bn (BatchNorma (None, 14, 14, 256) 1024 ['conv4_block19_1_conv[0][0]']
lization)
conv4_block19_1_relu (Activati (None, 14, 14, 256) 0 ['conv4_block19_1_bn[0][0]']
on)
conv4_block19_2_pad (ZeroPaddi (None, 16, 16, 256) 0 ['conv4_block19_1_relu[0][0]']
ng2D)
conv4_block19_2_conv (Conv2D) (None, 14, 14, 256) 589824 ['conv4_block19_2_pad[0][0]']
conv4_block19_2_bn (BatchNorma (None, 14, 14, 256) 1024 ['conv4_block19_2_conv[0][0]']
lization)
conv4_block19_2_relu (Activati (None, 14, 14, 256) 0 ['conv4_block19_2_bn[0][0]']
on)
conv4_block19_3_conv (Conv2D) (None, 14, 14, 1024 263168 ['conv4_block19_2_relu[0][0]']
)
conv4_block19_out (Add) (None, 14, 14, 1024 0 ['conv4_block18_out[0][0]',
) 'conv4_block19_3_conv[0][0]']
conv4_block20_preact_bn (Batch (None, 14, 14, 1024 4096 ['conv4_block19_out[0][0]']
Normalization) )
conv4_block20_preact_relu (Act (None, 14, 14, 1024 0 ['conv4_block20_preact_bn[0][0]']
ivation) )
conv4_block20_1_conv (Conv2D) (None, 14, 14, 256) 262144 ['conv4_block20_preact_relu[0][0]
']
conv4_block20_1_bn (BatchNorma (None, 14, 14, 256) 1024 ['conv4_block20_1_conv[0][0]']
lization)
conv4_block20_1_relu (Activati (None, 14, 14, 256) 0 ['conv4_block20_1_bn[0][0]']
on)
conv4_block20_2_pad (ZeroPaddi (None, 16, 16, 256) 0 ['conv4_block20_1_relu[0][0]']
ng2D)
conv4_block20_2_conv (Conv2D) (None, 14, 14, 256) 589824 ['conv4_block20_2_pad[0][0]']
conv4_block20_2_bn (BatchNorma (None, 14, 14, 256) 1024 ['conv4_block20_2_conv[0][0]']
lization)
conv4_block20_2_relu (Activati (None, 14, 14, 256) 0 ['conv4_block20_2_bn[0][0]']
on)
conv4_block20_3_conv (Conv2D) (None, 14, 14, 1024 263168 ['conv4_block20_2_relu[0][0]']
)
conv4_block20_out (Add) (None, 14, 14, 1024 0 ['conv4_block19_out[0][0]',
) 'conv4_block20_3_conv[0][0]']
conv4_block21_preact_bn (Batch (None, 14, 14, 1024 4096 ['conv4_block20_out[0][0]']
Normalization) )
conv4_block21_preact_relu (Act (None, 14, 14, 1024 0 ['conv4_block21_preact_bn[0][0]']
ivation) )
conv4_block21_1_conv (Conv2D) (None, 14, 14, 256) 262144 ['conv4_block21_preact_relu[0][0]
']
conv4_block21_1_bn (BatchNorma (None, 14, 14, 256) 1024 ['conv4_block21_1_conv[0][0]']
lization)
conv4_block21_1_relu (Activati (None, 14, 14, 256) 0 ['conv4_block21_1_bn[0][0]']
on)
conv4_block21_2_pad (ZeroPaddi (None, 16, 16, 256) 0 ['conv4_block21_1_relu[0][0]']
ng2D)
conv4_block21_2_conv (Conv2D) (None, 14, 14, 256) 589824 ['conv4_block21_2_pad[0][0]']
conv4_block21_2_bn (BatchNorma (None, 14, 14, 256) 1024 ['conv4_block21_2_conv[0][0]']
lization)
conv4_block21_2_relu (Activati (None, 14, 14, 256) 0 ['conv4_block21_2_bn[0][0]']
on)
conv4_block21_3_conv (Conv2D) (None, 14, 14, 1024 263168 ['conv4_block21_2_relu[0][0]']
)
conv4_block21_out (Add) (None, 14, 14, 1024 0 ['conv4_block20_out[0][0]',
) 'conv4_block21_3_conv[0][0]']
conv4_block22_preact_bn (Batch (None, 14, 14, 1024 4096 ['conv4_block21_out[0][0]']
Normalization) )
conv4_block22_preact_relu (Act (None, 14, 14, 1024 0 ['conv4_block22_preact_bn[0][0]']
ivation) )
conv4_block22_1_conv (Conv2D) (None, 14, 14, 256) 262144 ['conv4_block22_preact_relu[0][0]
']
conv4_block22_1_bn (BatchNorma (None, 14, 14, 256) 1024 ['conv4_block22_1_conv[0][0]']
lization)
conv4_block22_1_relu (Activati (None, 14, 14, 256) 0 ['conv4_block22_1_bn[0][0]']
on)
conv4_block22_2_pad (ZeroPaddi (None, 16, 16, 256) 0 ['conv4_block22_1_relu[0][0]']
ng2D)
conv4_block22_2_conv (Conv2D) (None, 14, 14, 256) 589824 ['conv4_block22_2_pad[0][0]']
conv4_block22_2_bn (BatchNorma (None, 14, 14, 256) 1024 ['conv4_block22_2_conv[0][0]']
lization)
conv4_block22_2_relu (Activati (None, 14, 14, 256) 0 ['conv4_block22_2_bn[0][0]']
on)
conv4_block22_3_conv (Conv2D) (None, 14, 14, 1024 263168 ['conv4_block22_2_relu[0][0]']
)
conv4_block22_out (Add) (None, 14, 14, 1024 0 ['conv4_block21_out[0][0]',
) 'conv4_block22_3_conv[0][0]']
conv4_block23_preact_bn (Batch (None, 14, 14, 1024 4096 ['conv4_block22_out[0][0]']
Normalization) )
conv4_block23_preact_relu (Act (None, 14, 14, 1024 0 ['conv4_block23_preact_bn[0][0]']
ivation) )
conv4_block23_1_conv (Conv2D) (None, 14, 14, 256) 262144 ['conv4_block23_preact_relu[0][0]
']
conv4_block23_1_bn (BatchNorma (None, 14, 14, 256) 1024 ['conv4_block23_1_conv[0][0]']
lization)
conv4_block23_1_relu (Activati (None, 14, 14, 256) 0 ['conv4_block23_1_bn[0][0]']
on)
conv4_block23_2_pad (ZeroPaddi (None, 16, 16, 256) 0 ['conv4_block23_1_relu[0][0]']
ng2D)
conv4_block23_2_conv (Conv2D) (None, 7, 7, 256) 589824 ['conv4_block23_2_pad[0][0]']
conv4_block23_2_bn (BatchNorma (None, 7, 7, 256) 1024 ['conv4_block23_2_conv[0][0]']
lization)
conv4_block23_2_relu (Activati (None, 7, 7, 256) 0 ['conv4_block23_2_bn[0][0]']
on)
max_pooling2d_5 (MaxPooling2D) (None, 7, 7, 1024) 0 ['conv4_block22_out[0][0]']
conv4_block23_3_conv (Conv2D) (None, 7, 7, 1024) 263168 ['conv4_block23_2_relu[0][0]']
conv4_block23_out (Add) (None, 7, 7, 1024) 0 ['max_pooling2d_5[0][0]',
'conv4_block23_3_conv[0][0]']
conv5_block1_preact_bn (BatchN (None, 7, 7, 1024) 4096 ['conv4_block23_out[0][0]']
ormalization)
conv5_block1_preact_relu (Acti (None, 7, 7, 1024) 0 ['conv5_block1_preact_bn[0][0]']
vation)
conv5_block1_1_conv (Conv2D) (None, 7, 7, 512) 524288 ['conv5_block1_preact_relu[0][0]'
]
conv5_block1_1_bn (BatchNormal (None, 7, 7, 512) 2048 ['conv5_block1_1_conv[0][0]']
ization)
conv5_block1_1_relu (Activatio (None, 7, 7, 512) 0 ['conv5_block1_1_bn[0][0]']
n)
conv5_block1_2_pad (ZeroPaddin (None, 9, 9, 512) 0 ['conv5_block1_1_relu[0][0]']
g2D)
conv5_block1_2_conv (Conv2D) (None, 7, 7, 512) 2359296 ['conv5_block1_2_pad[0][0]']
conv5_block1_2_bn (BatchNormal (None, 7, 7, 512) 2048 ['conv5_block1_2_conv[0][0]']
ization)
conv5_block1_2_relu (Activatio (None, 7, 7, 512) 0 ['conv5_block1_2_bn[0][0]']
n)
conv5_block1_0_conv (Conv2D) (None, 7, 7, 2048) 2099200 ['conv5_block1_preact_relu[0][0]'
]
conv5_block1_3_conv (Conv2D) (None, 7, 7, 2048) 1050624 ['conv5_block1_2_relu[0][0]']
conv5_block1_out (Add) (None, 7, 7, 2048) 0 ['conv5_block1_0_conv[0][0]',
'conv5_block1_3_conv[0][0]']
conv5_block2_preact_bn (BatchN (None, 7, 7, 2048) 8192 ['conv5_block1_out[0][0]']
ormalization)
conv5_block2_preact_relu (Acti (None, 7, 7, 2048) 0 ['conv5_block2_preact_bn[0][0]']
vation)
conv5_block2_1_conv (Conv2D) (None, 7, 7, 512) 1048576 ['conv5_block2_preact_relu[0][0]'
]
conv5_block2_1_bn (BatchNormal (None, 7, 7, 512) 2048 ['conv5_block2_1_conv[0][0]']
ization)
conv5_block2_1_relu (Activatio (None, 7, 7, 512) 0 ['conv5_block2_1_bn[0][0]']
n)
conv5_block2_2_pad (ZeroPaddin (None, 9, 9, 512) 0 ['conv5_block2_1_relu[0][0]']
g2D)
conv5_block2_2_conv (Conv2D) (None, 7, 7, 512) 2359296 ['conv5_block2_2_pad[0][0]']
conv5_block2_2_bn (BatchNormal (None, 7, 7, 512) 2048 ['conv5_block2_2_conv[0][0]']
ization)
conv5_block2_2_relu (Activatio (None, 7, 7, 512) 0 ['conv5_block2_2_bn[0][0]']
n)
conv5_block2_3_conv (Conv2D) (None, 7, 7, 2048) 1050624 ['conv5_block2_2_relu[0][0]']
conv5_block2_out (Add) (None, 7, 7, 2048) 0 ['conv5_block1_out[0][0]',
'conv5_block2_3_conv[0][0]']
conv5_block3_preact_bn (BatchN (None, 7, 7, 2048) 8192 ['conv5_block2_out[0][0]']
ormalization)
conv5_block3_preact_relu (Acti (None, 7, 7, 2048) 0 ['conv5_block3_preact_bn[0][0]']
vation)
conv5_block3_1_conv (Conv2D) (None, 7, 7, 512) 1048576 ['conv5_block3_preact_relu[0][0]'
]
conv5_block3_1_bn (BatchNormal (None, 7, 7, 512) 2048 ['conv5_block3_1_conv[0][0]']
ization)
conv5_block3_1_relu (Activatio (None, 7, 7, 512) 0 ['conv5_block3_1_bn[0][0]']
n)
conv5_block3_2_pad (ZeroPaddin (None, 9, 9, 512) 0 ['conv5_block3_1_relu[0][0]']
g2D)
conv5_block3_2_conv (Conv2D) (None, 7, 7, 512) 2359296 ['conv5_block3_2_pad[0][0]']
conv5_block3_2_bn (BatchNormal (None, 7, 7, 512) 2048 ['conv5_block3_2_conv[0][0]']
ization)
conv5_block3_2_relu (Activatio (None, 7, 7, 512) 0 ['conv5_block3_2_bn[0][0]']
n)
conv5_block3_3_conv (Conv2D) (None, 7, 7, 2048) 1050624 ['conv5_block3_2_relu[0][0]']
conv5_block3_out (Add) (None, 7, 7, 2048) 0 ['conv5_block2_out[0][0]',
'conv5_block3_3_conv[0][0]']
post_bn (BatchNormalization) (None, 7, 7, 2048) 8192 ['conv5_block3_out[0][0]']
post_relu (Activation) (None, 7, 7, 2048) 0 ['post_bn[0][0]']
flatten_4 (Flatten) (None, 100352) 0 ['post_relu[0][0]']
dense_4 (Dense) (None, 5) 501765 ['flatten_4[0][0]']
==================================================================================================
Total params: 43,128,325
Trainable params: 501,765
Non-trainable params: 42,626,560
__________________________________________________________________________________________________
# tell the model what cost and optimization method to use
model.compile(
loss='sparse_categorical_crossentropy',
optimizer='adam',
metrics=['accuracy']
)
#train_ds_vgg_sw, test_ds_vgg_sw, validation_ds_vgg_sw
# fit the model
r = model.fit_generator(
train_ds,
validation_data=validation_ds,
epochs=25,
steps_per_epoch=len(train_ds),
validation_steps=len(validation_ds)
)Epoch 1/25
/var/folders/3r/c8tg1h051m18qhsdccdysrt40000gn/T/ipykernel_11345/3602206220.py:10: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators. r = model.fit_generator(
25/25 [==============================] - 59s 2s/step - loss: 3.6952 - accuracy: 0.7675 - val_loss: 1.0397 - val_accuracy: 0.9427 Epoch 2/25 25/25 [==============================] - 55s 2s/step - loss: 0.2606 - accuracy: 0.9688 - val_loss: 0.6033 - val_accuracy: 0.9479 Epoch 3/25 25/25 [==============================] - 55s 2s/step - loss: 0.0624 - accuracy: 0.9887 - val_loss: 0.7021 - val_accuracy: 0.9323 Epoch 4/25 25/25 [==============================] - 55s 2s/step - loss: 0.0150 - accuracy: 0.9987 - val_loss: 0.4405 - val_accuracy: 0.9688 Epoch 5/25 25/25 [==============================] - 56s 2s/step - loss: 0.0123 - accuracy: 0.9975 - val_loss: 0.3344 - val_accuracy: 0.9740 Epoch 6/25 25/25 [==============================] - 56s 2s/step - loss: 1.9117e-07 - accuracy: 1.0000 - val_loss: 0.1343 - val_accuracy: 0.9844 Epoch 7/25 25/25 [==============================] - 56s 2s/step - loss: 4.4405e-08 - accuracy: 1.0000 - val_loss: 0.2787 - val_accuracy: 0.9844 Epoch 8/25 25/25 [==============================] - 56s 2s/step - loss: 3.5911e-08 - accuracy: 1.0000 - val_loss: 0.2785 - val_accuracy: 0.9844 Epoch 9/25 25/25 [==============================] - 57s 2s/step - loss: 2.7716e-08 - accuracy: 1.0000 - val_loss: 0.2785 - val_accuracy: 0.9844 Epoch 10/25 25/25 [==============================] - 57s 2s/step - loss: 2.2948e-08 - accuracy: 1.0000 - val_loss: 0.1292 - val_accuracy: 0.9896 Epoch 11/25 25/25 [==============================] - 57s 2s/step - loss: 2.0563e-08 - accuracy: 1.0000 - val_loss: 0.2785 - val_accuracy: 0.9844 Epoch 12/25 25/25 [==============================] - 57s 2s/step - loss: 1.7583e-08 - accuracy: 1.0000 - val_loss: 0.2785 - val_accuracy: 0.9844 Epoch 13/25 25/25 [==============================] - 60s 2s/step - loss: 1.5646e-08 - accuracy: 1.0000 - val_loss: 0.2775 - val_accuracy: 0.9844 Epoch 14/25 25/25 [==============================] - 57s 2s/step - loss: 1.4305e-08 - accuracy: 1.0000 - val_loss: 0.1950 - val_accuracy: 0.9896 Epoch 15/25 25/25 [==============================] - 57s 2s/step - loss: 1.3560e-08 - accuracy: 1.0000 - val_loss: 0.2785 - val_accuracy: 0.9844 Epoch 16/25 25/25 [==============================] - 57s 2s/step - loss: 1.1921e-08 - accuracy: 1.0000 - val_loss: 0.2785 - val_accuracy: 0.9844 Epoch 17/25 25/25 [==============================] - 57s 2s/step - loss: 1.1176e-08 - accuracy: 1.0000 - val_loss: 0.1318 - val_accuracy: 0.9896 Epoch 18/25 25/25 [==============================] - 59s 2s/step - loss: 1.0431e-08 - accuracy: 1.0000 - val_loss: 0.2776 - val_accuracy: 0.9844 Epoch 19/25 25/25 [==============================] - 58s 2s/step - loss: 9.8347e-09 - accuracy: 1.0000 - val_loss: 0.2785 - val_accuracy: 0.9844 Epoch 20/25 25/25 [==============================] - 58s 2s/step - loss: 9.2387e-09 - accuracy: 1.0000 - val_loss: 0.2775 - val_accuracy: 0.9844 Epoch 21/25 25/25 [==============================] - 60s 2s/step - loss: 8.7917e-09 - accuracy: 1.0000 - val_loss: 0.2785 - val_accuracy: 0.9844 Epoch 22/25 25/25 [==============================] - 58s 2s/step - loss: 8.3446e-09 - accuracy: 1.0000 - val_loss: 0.2785 - val_accuracy: 0.9844 Epoch 23/25 25/25 [==============================] - 57s 2s/step - loss: 5.5134e-09 - accuracy: 1.0000 - val_loss: 0.2785 - val_accuracy: 0.9844 Epoch 24/25 25/25 [==============================] - 56s 2s/step - loss: 7.5996e-09 - accuracy: 1.0000 - val_loss: 0.2785 - val_accuracy: 0.9844 Epoch 25/25 25/25 [==============================] - 57s 2s/step - loss: 7.3016e-09 - accuracy: 1.0000 - val_loss: 0.2785 - val_accuracy: 0.9844
# loss
plt.plot(r.history["accuracy"])
plt.plot(r.history['val_accuracy'])
plt.plot(r.history['loss'])
plt.plot(r.history['val_loss'])
plt.title("Model accuracy")
plt.ylabel("Value")
plt.xlabel("Epoch")
plt.legend(["Accuracy","Validation Accuracy","Loss","Validation Loss"])
plt.show()
model.save('resnet_1.h5')model.evaluate(test_ds)8/8 [==============================] - 15s 2s/step - loss: 0.7370 - accuracy: 0.9414
[0.7369823455810547, 0.94140625]