File size: 1,991 Bytes
171301a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 | import os
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout
from tensorflow.keras.optimizers import Adam
data_dir = "Dataset"
classes = ["Alluvial", "Black", "Clay", "Red"]
img_height, img_width = 224, 224
batch_size = 32
epochs = 10
datagen = ImageDataGenerator(rescale=1./255, validation_split=0.2)
train_data = datagen.flow_from_directory(
data_dir,
target_size=(img_height, img_width),
batch_size=batch_size,
class_mode='categorical',
subset='training'
)
val_data = datagen.flow_from_directory(
data_dir,
target_size=(img_height, img_width),
batch_size=batch_size,
class_mode='categorical',
subset='validation'
)
model = Sequential([
Conv2D(32, (3, 3), activation='relu', input_shape=(img_height, img_width, 3)),
MaxPooling2D(2, 2),
Conv2D(64, (3, 3), activation='relu'),
MaxPooling2D(2, 2),
Conv2D(128, (3, 3), activation='relu'),
MaxPooling2D(2, 2),
Flatten(),
Dense(128, activation='relu'),
Dropout(0.5),
Dense(len(classes), activation='softmax')
])
model.compile(optimizer=Adam(),
loss='categorical_crossentropy',
metrics=['accuracy'])
history = model.fit(
train_data,
validation_data=val_data,
epochs=epochs
)
model.save("SoilNet.keras")
plt.figure(figsize=(10, 4))
plt.subplot(1, 2, 1)
plt.plot(history.history['accuracy'], label='Train Accuracy')
plt.plot(history.history['val_accuracy'], label='Validation Accuracy')
plt.title('Accuracy')
plt.legend()
plt.subplot(1, 2, 2)
plt.plot(history.history['loss'], label='Train Loss')
plt.plot(history.history['val_loss'], label='Validation Loss')
plt.title('Loss')
plt.legend()
plt.tight_layout()
plt.show()
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