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feature/load-dataset
Detection-of-plant-diseases/testing.ipynb

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# This script needs these libraries to be installed:
#   tensorflow, numpy

import wandb
from wandb.keras import WandbMetricsLogger, WandbModelCheckpoint

import random
import numpy as np
import tensorflow as tf


# Start a run, tracking hyperparameters
wandb.init(
    # set the wandb project where this run will be logged
    project="Detection of plant diseases",

    # track hyperparameters and run metadata with wandb.config
    config={
        "layer_1": 512,
        "activation_1": "relu",
        "dropout": random.uniform(0.01, 0.80),
        "layer_2": 10,
        "activation_2": "softmax",
        "optimizer": "sgd",
        "loss": "sparse_categorical_crossentropy",
        "metric": "accuracy",
        "epoch": 8,
        "batch_size": 256
    }
)

# [optional] use wandb.config as your config
config = wandb.config

# get the data
mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0
x_train, y_train = x_train[::5], y_train[::5]
x_test, y_test = x_test[::20], y_test[::20]
labels = [str(digit) for digit in range(np.max(y_train) + 1)]

# build a model
model = tf.keras.models.Sequential([
    tf.keras.layers.Flatten(input_shape=(28, 28)),
    tf.keras.layers.Dense(config.layer_1, activation=config.activation_1),
    tf.keras.layers.Dropout(config.dropout),
    tf.keras.layers.Dense(config.layer_2, activation=config.activation_2)
    ])

# compile the model
model.compile(optimizer=config.optimizer,
              loss=config.loss,
              metrics=[config.metric]
              )

# WandbMetricsLogger will log train and validation metrics to wandb
# WandbModelCheckpoint will upload model checkpoints to wandb
history = model.fit(x=x_train, y=y_train,
                    epochs=config.epoch,
                    batch_size=config.batch_size,
                    validation_data=(x_test, y_test),
                    callbacks=[
                      WandbMetricsLogger(log_freq=5),
                      WandbModelCheckpoint("models")
                    ])

# [optional] finish the wandb run, necessary in notebooks
wandb.finish()
Tracking run with wandb version 0.16.6
Run data is saved locally in /mnt/c/Users/krzys/OneDrive/Studia/inz-uczenia-maszynowego/Detection-of-plant-diseases/wandb/run-20240416_232247-bfji8amn
Syncing run floral-energy-3 to Weights & Biases (docs)
View project at https://wandb.ai/uczenie-maszynowe-projekt/Detection%20of%20plant%20diseases
View run at https://wandb.ai/uczenie-maszynowe-projekt/Detection%20of%20plant%20diseases/runs/bfji8amn
Epoch 1/8
44/47 [===========================>..] - ETA: 0s - loss: 2.1872 - accuracy: 0.2224INFO:tensorflow:Assets written to: models/assets

INFO:tensorflow:Assets written to: models/assets
wandb: Adding directory to artifact (./models)... Done. 0.1s

47/47 [==============================] - 2s 32ms/step - loss: 2.1734 - accuracy: 0.2344 - val_loss: 1.9111 - val_accuracy: 0.5380
Epoch 2/8
40/47 [========================>.....] - ETA: 0s - loss: 1.7703 - accuracy: 0.5437INFO:tensorflow:Assets written to: models/assets

INFO:tensorflow:Assets written to: models/assets
wandb: Adding directory to artifact (./models)... Done. 0.1s

47/47 [==============================] - 1s 31ms/step - loss: 1.7483 - accuracy: 0.5527 - val_loss: 1.5486 - val_accuracy: 0.6880
Epoch 3/8
46/47 [============================>.] - ETA: 0s - loss: 1.4466 - accuracy: 0.6818INFO:tensorflow:Assets written to: models/assets

INFO:tensorflow:Assets written to: models/assets
wandb: Adding directory to artifact (./models)... Done. 0.1s

47/47 [==============================] - 2s 33ms/step - loss: 1.4444 - accuracy: 0.6829 - val_loss: 1.2824 - val_accuracy: 0.7460
Epoch 4/8
44/47 [===========================>..] - ETA: 0s - loss: 1.2232 - accuracy: 0.7362INFO:tensorflow:Assets written to: models/assets

INFO:tensorflow:Assets written to: models/assets
wandb: Adding directory to artifact (./models)... Done. 0.1s

47/47 [==============================] - 2s 32ms/step - loss: 1.2162 - accuracy: 0.7390 - val_loss: 1.0886 - val_accuracy: 0.7880
Epoch 5/8
44/47 [===========================>..] - ETA: 0s - loss: 1.0583 - accuracy: 0.7694INFO:tensorflow:Assets written to: models/assets

INFO:tensorflow:Assets written to: models/assets
wandb: Adding directory to artifact (./models)... Done. 0.1s

47/47 [==============================] - 1s 28ms/step - loss: 1.0519 - accuracy: 0.7711 - val_loss: 0.9497 - val_accuracy: 0.8020
Epoch 6/8
41/47 [=========================>....] - ETA: 0s - loss: 0.9382 - accuracy: 0.7897INFO:tensorflow:Assets written to: models/assets

INFO:tensorflow:Assets written to: models/assets
wandb: Adding directory to artifact (./models)... Done. 0.1s

47/47 [==============================] - 1s 28ms/step - loss: 0.9339 - accuracy: 0.7902 - val_loss: 0.8484 - val_accuracy: 0.8180
Epoch 7/8
47/47 [==============================] - ETA: 0s - loss: 0.8496 - accuracy: 0.8043INFO:tensorflow:Assets written to: models/assets

INFO:tensorflow:Assets written to: models/assets
wandb: Adding directory to artifact (./models)... Done. 0.1s

47/47 [==============================] - 1s 27ms/step - loss: 0.8496 - accuracy: 0.8043 - val_loss: 0.7735 - val_accuracy: 0.8220
Epoch 8/8
44/47 [===========================>..] - ETA: 0s - loss: 0.7790 - accuracy: 0.8180INFO:tensorflow:Assets written to: models/assets

INFO:tensorflow:Assets written to: models/assets
wandb: Adding directory to artifact (./models)... Done. 0.1s


47/47 [==============================] - 1s 29ms/step - loss: 0.7779 - accuracy: 0.8183 - val_loss: 0.7165 - val_accuracy: 0.8260

Run history:


batch/accuracy▁▁▁▂▂▄▅▅▅▅▆▆▆▇▇▇▇▇▇▇▇▇▇▇████████████████
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Run summary:


batch/accuracy0.81726
batch/batch_step395
batch/learning_rate0.01
batch/loss0.77969
epoch/accuracy0.81825
epoch/epoch7
epoch/learning_rate0.01
epoch/loss0.77791
epoch/val_accuracy0.826
epoch/val_loss0.71648

View run floral-energy-3 at: https://wandb.ai/uczenie-maszynowe-projekt/Detection%20of%20plant%20diseases/runs/bfji8amn
View project at: https://wandb.ai/uczenie-maszynowe-projekt/Detection%20of%20plant%20diseases
Synced 5 W&B file(s), 0 media file(s), 42 artifact file(s) and 0 other file(s)
Find logs at: ./wandb/run-20240416_232247-bfji8amn/logs