23 KiB
23 KiB
from IPython.display import Image, SVG, display
from tqdm import tqdm
import matplotlib.pyplot as plt
import sys
import subprocess
import pkg_resources
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
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
import tensorflow as tf
from tensorflow import keras
import os
import timefrom training.data_load import load_data
train_ds, test_ds, validation_ds = load_data((227, 227), './new_data_transformed/train', './new_data_transformed/test')def tensorboard_callback(model_name):
return keras.callbacks.TensorBoard(os.path.join(f"./logs/{model_name}", time.strftime("run_%Y_%m_%d-%H_%M_%S")))model = keras.models.Sequential([
keras.layers.Conv2D(filters=96, kernel_size=(11,11), strides=(4,4), activation='relu', input_shape=(227,227,3)),
keras.layers.BatchNormalization(),
keras.layers.MaxPool2D(pool_size=(3,3), strides=(2,2)),
keras.layers.Conv2D(filters=256, kernel_size=(5,5), strides=(1,1), activation='relu', padding="same"),
keras.layers.BatchNormalization(),
keras.layers.MaxPool2D(pool_size=(3,3), strides=(2,2)),
keras.layers.Conv2D(filters=384, kernel_size=(3,3), strides=(1,1), activation='relu', padding="same"),
keras.layers.BatchNormalization(),
keras.layers.Conv2D(filters=384, kernel_size=(3,3), strides=(1,1), activation='relu', padding="same"),
keras.layers.BatchNormalization(),
keras.layers.Conv2D(filters=256, kernel_size=(3,3), strides=(1,1), activation='relu', padding="same"),
keras.layers.BatchNormalization(),
keras.layers.MaxPool2D(pool_size=(3,3), strides=(2,2)),
keras.layers.Flatten(),
keras.layers.Dense(4096, activation='relu'),
keras.layers.Dense(4096, activation='relu'),
keras.layers.Dense(6, activation='softmax')
])model.compile(loss='sparse_categorical_crossentropy', optimizer=tf.optimizers.SGD(learning_rate=.001), metrics=['accuracy'])
model.summary()Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d (Conv2D) (None, 55, 55, 96) 34944
batch_normalization (BatchN (None, 55, 55, 96) 384
ormalization)
max_pooling2d (MaxPooling2D (None, 27, 27, 96) 0
)
conv2d_1 (Conv2D) (None, 27, 27, 256) 614656
batch_normalization_1 (Batc (None, 27, 27, 256) 1024
hNormalization)
max_pooling2d_1 (MaxPooling (None, 13, 13, 256) 0
2D)
conv2d_2 (Conv2D) (None, 13, 13, 384) 885120
batch_normalization_2 (Batc (None, 13, 13, 384) 1536
hNormalization)
conv2d_3 (Conv2D) (None, 13, 13, 384) 1327488
batch_normalization_3 (Batc (None, 13, 13, 384) 1536
hNormalization)
conv2d_4 (Conv2D) (None, 13, 13, 256) 884992
batch_normalization_4 (Batc (None, 13, 13, 256) 1024
hNormalization)
max_pooling2d_2 (MaxPooling (None, 6, 6, 256) 0
2D)
flatten (Flatten) (None, 9216) 0
dense (Dense) (None, 4096) 37752832
dense_1 (Dense) (None, 4096) 16781312
dense_2 (Dense) (None, 6) 24582
=================================================================
Total params: 58,311,430
Trainable params: 58,308,678
Non-trainable params: 2,752
_________________________________________________________________
model.fit(train_ds,
epochs=50,
validation_data=validation_ds,
validation_freq=1,
callbacks=[tensorboard_callback("AlexNet")])Epoch 1/50 45/45 [==============================] - 13s 82ms/step - loss: 1.5723 - accuracy: 0.4535 - val_loss: 1.8094 - val_accuracy: 0.1676 Epoch 2/50 45/45 [==============================] - 3s 60ms/step - loss: 0.8888 - accuracy: 0.6771 - val_loss: 1.8356 - val_accuracy: 0.1733 Epoch 3/50 45/45 [==============================] - 3s 62ms/step - loss: 0.7100 - accuracy: 0.7292 - val_loss: 1.8753 - val_accuracy: 0.1534 Epoch 4/50 45/45 [==============================] - 3s 61ms/step - loss: 0.5200 - accuracy: 0.8222 - val_loss: 1.9177 - val_accuracy: 0.1477 Epoch 5/50 45/45 [==============================] - 3s 60ms/step - loss: 0.3887 - accuracy: 0.8868 - val_loss: 2.0252 - val_accuracy: 0.1591 Epoch 6/50 45/45 [==============================] - 3s 60ms/step - loss: 0.2841 - accuracy: 0.9340 - val_loss: 2.0583 - val_accuracy: 0.2273 Epoch 7/50 45/45 [==============================] - 3s 60ms/step - loss: 0.2114 - accuracy: 0.9569 - val_loss: 2.1366 - val_accuracy: 0.2216 Epoch 8/50 45/45 [==============================] - 3s 60ms/step - loss: 0.1738 - accuracy: 0.9764 - val_loss: 2.1499 - val_accuracy: 0.3153 Epoch 9/50 45/45 [==============================] - 3s 60ms/step - loss: 0.1298 - accuracy: 0.9854 - val_loss: 2.0241 - val_accuracy: 0.3693 Epoch 10/50 45/45 [==============================] - 3s 60ms/step - loss: 0.1152 - accuracy: 0.9896 - val_loss: 1.5216 - val_accuracy: 0.4631 Epoch 11/50 45/45 [==============================] - 3s 60ms/step - loss: 0.0893 - accuracy: 0.9958 - val_loss: 1.2411 - val_accuracy: 0.5511 Epoch 12/50 45/45 [==============================] - 3s 61ms/step - loss: 0.0922 - accuracy: 0.9910 - val_loss: 0.9918 - val_accuracy: 0.6477 Epoch 13/50 45/45 [==============================] - 3s 61ms/step - loss: 0.0665 - accuracy: 0.9972 - val_loss: 0.9535 - val_accuracy: 0.6648 Epoch 14/50 45/45 [==============================] - 3s 60ms/step - loss: 0.0628 - accuracy: 0.9965 - val_loss: 0.7136 - val_accuracy: 0.7415 Epoch 15/50 45/45 [==============================] - 3s 60ms/step - loss: 0.0624 - accuracy: 0.9972 - val_loss: 0.6692 - val_accuracy: 0.7642 Epoch 16/50 45/45 [==============================] - 3s 60ms/step - loss: 0.0534 - accuracy: 0.9979 - val_loss: 0.6320 - val_accuracy: 0.7898 Epoch 17/50 45/45 [==============================] - 3s 60ms/step - loss: 0.0436 - accuracy: 0.9993 - val_loss: 0.6613 - val_accuracy: 0.7841 Epoch 18/50 45/45 [==============================] - 3s 60ms/step - loss: 0.0401 - accuracy: 0.9972 - val_loss: 0.6103 - val_accuracy: 0.7869 Epoch 19/50 45/45 [==============================] - 3s 61ms/step - loss: 0.0314 - accuracy: 0.9993 - val_loss: 0.6222 - val_accuracy: 0.8040 Epoch 20/50 45/45 [==============================] - 3s 61ms/step - loss: 0.0308 - accuracy: 0.9986 - val_loss: 0.6182 - val_accuracy: 0.7841 Epoch 21/50 45/45 [==============================] - 3s 60ms/step - loss: 0.0309 - accuracy: 0.9993 - val_loss: 0.6246 - val_accuracy: 0.7926 Epoch 22/50 45/45 [==============================] - 3s 61ms/step - loss: 0.0256 - accuracy: 1.0000 - val_loss: 0.6276 - val_accuracy: 0.7983 Epoch 23/50 45/45 [==============================] - 3s 61ms/step - loss: 0.0259 - accuracy: 1.0000 - val_loss: 0.6332 - val_accuracy: 0.8011 Epoch 24/50 45/45 [==============================] - 3s 62ms/step - loss: 0.0223 - accuracy: 1.0000 - val_loss: 0.6229 - val_accuracy: 0.7955 Epoch 25/50 45/45 [==============================] - 3s 61ms/step - loss: 0.0225 - accuracy: 1.0000 - val_loss: 0.6089 - val_accuracy: 0.8011 Epoch 26/50 45/45 [==============================] - 3s 61ms/step - loss: 0.0217 - accuracy: 1.0000 - val_loss: 0.6463 - val_accuracy: 0.7926 Epoch 27/50 45/45 [==============================] - 3s 60ms/step - loss: 0.0221 - accuracy: 1.0000 - val_loss: 0.6237 - val_accuracy: 0.8068 Epoch 28/50 45/45 [==============================] - 3s 61ms/step - loss: 0.0196 - accuracy: 1.0000 - val_loss: 0.6484 - val_accuracy: 0.7898 Epoch 29/50 45/45 [==============================] - 3s 61ms/step - loss: 0.0204 - accuracy: 0.9993 - val_loss: 0.6200 - val_accuracy: 0.7926 Epoch 30/50 45/45 [==============================] - 3s 60ms/step - loss: 0.0194 - accuracy: 0.9993 - val_loss: 0.6186 - val_accuracy: 0.8040 Epoch 31/50 45/45 [==============================] - 3s 61ms/step - loss: 0.0171 - accuracy: 1.0000 - val_loss: 0.6418 - val_accuracy: 0.8068 Epoch 32/50 45/45 [==============================] - 3s 62ms/step - loss: 0.0172 - accuracy: 1.0000 - val_loss: 0.6234 - val_accuracy: 0.8011 Epoch 33/50 45/45 [==============================] - 3s 61ms/step - loss: 0.0168 - accuracy: 1.0000 - val_loss: 0.6278 - val_accuracy: 0.8068 Epoch 34/50 45/45 [==============================] - 3s 61ms/step - loss: 0.0153 - accuracy: 1.0000 - val_loss: 0.6527 - val_accuracy: 0.7898 Epoch 35/50 45/45 [==============================] - 3s 61ms/step - loss: 0.0138 - accuracy: 1.0000 - val_loss: 0.6198 - val_accuracy: 0.7926 Epoch 36/50 45/45 [==============================] - 3s 61ms/step - loss: 0.0130 - accuracy: 1.0000 - val_loss: 0.6359 - val_accuracy: 0.7869 Epoch 37/50 45/45 [==============================] - 3s 61ms/step - loss: 0.0130 - accuracy: 1.0000 - val_loss: 0.6123 - val_accuracy: 0.8153 Epoch 38/50 45/45 [==============================] - 3s 61ms/step - loss: 0.0116 - accuracy: 1.0000 - val_loss: 0.6061 - val_accuracy: 0.8040 Epoch 39/50 45/45 [==============================] - 3s 61ms/step - loss: 0.0113 - accuracy: 1.0000 - val_loss: 0.6256 - val_accuracy: 0.8011 Epoch 40/50 45/45 [==============================] - 3s 61ms/step - loss: 0.0142 - accuracy: 0.9993 - val_loss: 0.6386 - val_accuracy: 0.8011 Epoch 41/50 45/45 [==============================] - 3s 62ms/step - loss: 0.0119 - accuracy: 1.0000 - val_loss: 0.6137 - val_accuracy: 0.8040 Epoch 42/50 45/45 [==============================] - 3s 61ms/step - loss: 0.0100 - accuracy: 1.0000 - val_loss: 0.6392 - val_accuracy: 0.8068 Epoch 43/50 45/45 [==============================] - 3s 62ms/step - loss: 0.0098 - accuracy: 1.0000 - val_loss: 0.6461 - val_accuracy: 0.8097 Epoch 44/50 45/45 [==============================] - 3s 61ms/step - loss: 0.0113 - accuracy: 1.0000 - val_loss: 0.6131 - val_accuracy: 0.8125 Epoch 45/50 45/45 [==============================] - 3s 61ms/step - loss: 0.0095 - accuracy: 1.0000 - val_loss: 0.6376 - val_accuracy: 0.8125 Epoch 46/50 45/45 [==============================] - 3s 61ms/step - loss: 0.0117 - accuracy: 0.9986 - val_loss: 0.6414 - val_accuracy: 0.7841 Epoch 47/50 45/45 [==============================] - 3s 61ms/step - loss: 0.0094 - accuracy: 1.0000 - val_loss: 0.6224 - val_accuracy: 0.8068 Epoch 48/50 45/45 [==============================] - 3s 61ms/step - loss: 0.0095 - accuracy: 1.0000 - val_loss: 0.5973 - val_accuracy: 0.8153 Epoch 49/50 45/45 [==============================] - 3s 62ms/step - loss: 0.0088 - accuracy: 1.0000 - val_loss: 0.6366 - val_accuracy: 0.8068 Epoch 50/50 45/45 [==============================] - 3s 61ms/step - loss: 0.0093 - accuracy: 1.0000 - val_loss: 0.6526 - val_accuracy: 0.8040
<keras.callbacks.History at 0x7efd9022ee80>
!tensorboard dev upload --logdir logs/AlexNet --name AlexNetFish***** TensorBoard Uploader ***** This will upload your TensorBoard logs to https://tensorboard.dev/ from the following directory: logs/AlexNet This TensorBoard will be visible to everyone. Do not upload sensitive data. Your use of this service is subject to Google's Terms of Service <https://policies.google.com/terms> and Privacy Policy <https://policies.google.com/privacy>, and TensorBoard.dev's Terms of Service <https://tensorboard.dev/policy/terms/>. This notice will not be shown again while you are logged into the uploader. To log out, run `tensorboard dev auth revoke`. Continue? (yes/NO) yes Please visit this URL to authorize this application: https://accounts.google.com/o/oauth2/auth?response_type=code&client_id=373649185512-8v619h5kft38l4456nm2dj4ubeqsrvh6.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=openid+https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fuserinfo.email&state=4s45OaCdh7CXhhMYUBHIgm6u6oiMTE&prompt=consent&access_type=offline Enter the authorization code: 4/1AWtgzh59_rKzQT7gGHgAyOnRtMf7ppSZuYlb-25UkSRY4IdlSjRmYmH_AfE Upload started and will continue reading any new data as it's added to the logdir. To stop uploading, press Ctrl-C. New experiment created. View your TensorBoard at: https://tensorboard.dev/experiment/0xVh6RMqTQiv0BCdbdLLBg/ [1m[2023-02-01T08:10:10][0m Started scanning logdir. [1m[2023-02-01T08:10:11][0m Total uploaded: 300 scalars, 0 tensors, 1 binary objects (75.6 kB) Interrupted. View your TensorBoard at https://tensorboard.dev/experiment/0xVh6RMqTQiv0BCdbdLLBg/ Traceback (most recent call last): File "/usr/local/bin/tensorboard", line 8, in <module> sys.exit(run_main()) File "/usr/local/lib/python3.8/dist-packages/tensorboard/main.py", line 46, in run_main app.run(tensorboard.main, flags_parser=tensorboard.configure) File "/usr/local/lib/python3.8/dist-packages/absl/app.py", line 308, in run _run_main(main, args) File "/usr/local/lib/python3.8/dist-packages/absl/app.py", line 254, in _run_main sys.exit(main(argv)) File "/usr/local/lib/python3.8/dist-packages/tensorboard/program.py", line 276, in main return runner(self.flags) or 0 File "/usr/local/lib/python3.8/dist-packages/tensorboard/uploader/uploader_subcommand.py", line 692, in run return _run(flags, self._experiment_url_callback) File "/usr/local/lib/python3.8/dist-packages/tensorboard/uploader/uploader_subcommand.py", line 125, in _run intent.execute(server_info, channel) File "/usr/local/lib/python3.8/dist-packages/tensorboard/uploader/uploader_subcommand.py", line 508, in execute sys.stdout.write(end_message + "\n") KeyboardInterrupt ^C
model.evaluate(test_ds)
9/9 [==============================] - 0s 32ms/step - loss: 0.9122 - accuracy: 0.7188
[0.9122310280799866, 0.71875]
from training.model_freeze import freeze_model
freeze_model(model, './frozen_models', "frozen_alex_net")