90 lines
2.5 KiB
Python
90 lines
2.5 KiB
Python
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#https://youtu.be/V61xy1ZnVTM?si=ZpPwSP5eOnaItPn2
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#https://www.kaggle.com/datasets/fadwateimi/food-plates2
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import numpy as np
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#import pandas as pd
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import matplotlib.pyplot as plt
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import tensorflow as tf
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from tensorflow import keras
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from tensorflow.keras import layers
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def wykres(epochs_size):
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epochs_range = range(epochs_size)
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plt.figure(figsize=(8,8))
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plt.subplot(1,2,1)
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plt.plot(epochs_range,history.history['accuracy'], label = 'Training Accuracy')
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plt.plot(epochs_range, history.history['val_accuracy'], label='Validation Accuracy')
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plt.title('Accuracy')
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plt.subplot(1, 2, 2)
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plt.plot(epochs_range, history.history['loss'], label='Training Loss')
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plt.plot(epochs_range, history.history['val_loss'], label='Validation Loss')
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plt.title('Loss')
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plt.show()
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trainset_path = r"Automatyczny_kelner/trainset"
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testset_path = r"Automatyczny_kelner/testset"
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useset_path = r"Automatyczny_kelner/validationset"
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img_width = 180
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img_height = 180
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trainset = tf.keras.utils.image_dataset_from_directory(
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trainset_path,
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shuffle = True,
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image_size = (img_width, img_height),
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batch_size = 32,
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validation_split = False
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)
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data_cat = trainset.class_names
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useset = tf.keras.utils.image_dataset_from_directory(
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useset_path,
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shuffle = True,
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image_size = (img_height, img_width),
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batch_size = 32,
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validation_split = False
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)
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testset = tf.keras.utils.image_dataset_from_directory(
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testset_path,
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shuffle = True,
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image_size = (img_height, img_width),
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batch_size = 32,
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validation_split = False
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)
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plt.figure(figsize = (10, 10))
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for image, labels in trainset.take(1):
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for i in range(4):
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plt.subplot(1, 4, i+1)
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plt.imshow(image[i].numpy().astype('uint8'))
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plt.title(data_cat[labels[i]])
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from tensorflow.keras.models import Sequential
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model = Sequential([
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layers.Rescaling(1./255),
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layers.Conv2D(16, 3, padding='same', activation='relu'),
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layers.MaxPooling2D(),
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layers.Conv2D(32, 3, padding='same', activation='relu'),
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layers.MaxPooling2D(),
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layers.Conv2D(64, 3, padding='same', activation='relu'),
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layers.MaxPooling2D(),
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layers.Flatten(),
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layers.Dropout(0.2),
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layers.Dense(128),
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layers.Dense(len(data_cat))
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])
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model.compile(optimizer='adam', loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), metrics=['accuracy'])
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epochs_size = 25
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history = model.fit(trainset, validation_data = useset, epochs = epochs_size)
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model.save("nn.keras")
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