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Delete 'main.py'

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Yahor Haleznik 2023-03-23 12:31:55 +01:00
parent a56373b33f
commit 98ad009b91
1 changed files with 0 additions and 51 deletions
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main.py
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import numpy as np
import tensorflow as tf
from tensorflow import keras
def normalize(image, label):
return image / 255, label
directoryTRAIN = "C:/Users/KimD/PycharmProjects/Traktor_V1/Vegetable Images/train"
directoryVALIDATION = "C:/Users/KimD/PycharmProjects/Traktor_V1/Vegetable Images/validation"
train_ds = tf.keras.utils.image_dataset_from_directory(directoryTRAIN,
seed=123, batch_size=32,
image_size=(224, 224), color_mode='rgb')
val_ds = tf.keras.utils.image_dataset_from_directory(directoryVALIDATION,
seed=123, batch_size=32,
image_size=(224, 224), color_mode='rgb')
train_ds = train_ds.map(normalize)
val_ds = val_ds.map(normalize)
model = keras.Sequential([
keras.layers.Conv2D(64, (3, 3), activation='relu', input_shape=(224, 224, 3)),
keras.layers.MaxPool2D((2, 2)),
keras.layers.Conv2D(128, (3, 3), activation='relu'),
keras.layers.MaxPool2D((2, 2)),
keras.layers.Conv2D(256, (3, 3), activation='relu'),
keras.layers.MaxPool2D((2, 2)),
keras.layers.Flatten(),
keras.layers.Dense(1024, activation='relu'),
keras.layers.Dense(9, activation='softmax')
])
print(model.summary())
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
trainHistory = model.fit(train_ds, epochs=4, validation_data=val_ds)
model = keras.models.load_model("C:/Users/KimD/PycharmProjects/Traktor_V1/mode2.h5")
(loss, accuracy) = model.evaluate(val_ds)
print(loss)
print(accuracy)
# model.save("mode2.h5")