CNN卷积神经网络花朵识别（5种）

• 花匠小妙招
• 2024-12-15 02:43

import tensorflow as tf

from tensorflow import keras

from tensorflow.keras import layers

from tensorflow.keras.preprocessing.image import ImageDataGenerator

import matplotlib.pyplot as plt

from tqdm import tqdm

dataset_path = "./flowers"

image_size = (320, 240)

batch_size = 32

num_classes = 5

epochs=10

datagen = ImageDataGenerator(

rescale=1./255,

validation_split=0.2

)

train_generator = datagen.flow_from_directory(

dataset_path,

target_size=image_size,

batch_size=batch_size,

class_mode='categorical',

subset='training'

)

validation_generator = datagen.flow_from_directory(

dataset_path,

target_size=image_size,

batch_size=batch_size,

class_mode='categorical',

subset='validation'

)

sample_images, _ = next(train_generator)

plt.figure(figsize=(10, 10))

for i in range(9):

plt.subplot(3, 3, i + 1)

plt.imshow(sample_images[i])

plt.axis("off")

plt.show()

sample_images, _ = next(validation_generator)

plt.figure(figsize=(10, 10))

for i in range(9):

plt.subplot(3, 3, i + 1)

plt.imshow(sample_images[i])

plt.axis("off")

plt.show()

model = keras.Sequential()

model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(image_size[0], image_size[1], 3)))

model.add(layers.MaxPooling2D((2, 2)))

model.add(layers.Conv2D(64, (3, 3), activation='relu'))

model.add(layers.MaxPooling2D((2, 2)))

model.add(layers.Conv2D(128, (3, 3), activation='relu'))

model.add(layers.MaxPooling2D((2, 2)))

model.add(layers.Flatten())

model.add(layers.Dense(256, activation='relu'))

model.add(layers.Dropout(0.5))

model.add(layers.Dense(num_classes, activation='softmax'))

model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

train_steps_per_epoch = train_generator.samples // batch_size

val_steps_per_epoch = validation_generator.samples // batch_size

for epoch in range(epochs):

print("Epoch {}/{}".format(epoch + 1, epochs))

pbar = tqdm(total=train_steps_per_epoch, unit="batch")

for _ in range(train_steps_per_epoch):

x_batch, y_batch = next(train_generator)

loss, acc = model.train_on_batch(x_batch, y_batch)

pbar.update(1)

pbar.set_description("Train loss: {:.4f}, acc: {:.4f}".format(loss, acc))

pbar.close()

val_loss, val_acc = model.evaluate(validation_generator, steps=val_steps_per_epoch)

print("Validation loss: {:.4f}, acc: {:.4f}".format(val_loss, val_acc))

model.save("flower_classification_model.h5")

test_image_path = "./flowers/test3-t.jpg"

test_image = tf.keras.preprocessing.image.load_img(test_image_path, target_size=image_size)

test_image_array = tf.keras.preprocessing.image.img_to_array(test_image)

test_image_array = tf.expand_dims(test_image_array, 0)

saved_model = keras.models.load_model("flower_classification_model.h5")

predictions = saved_model.predict(test_image_array)

predicted_class_index = tf.argmax(predictions, axis=-1)

predicted_class = train_generator.class_indices

for class_name, class_index in predicted_class.items():

if class_index == predicted_class_index:

print("预测结果：", class_name)

break

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