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845天前### 先安装的依赖： |
#TensorFlow |
#Keras |
#Matplotlib |
#Scikit-Learn |
# 导入所需的库 |
import tensorflow as tf |
from tensorflow.keras.models import Sequential |
from tensorflow.keras.layers import Conv2D, MaxPooling2D |
from tensorflow.keras.layers import Activation, Dropout, Flatten, Dense |
from tensorflow.keras.optimizers import Adam |
import matplotlib.pyplot as plt |
import numpy as np |
from sklearn.model_selection import train_test_split |
# 加载和准备数据集 |
X = np.load("animals.npy") |
y = np.load("labels.npy") |
# 将数据集分为训练集和测试集 |
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) |
# 创建卷积模型 |
model = Sequential() |
model.add(Conv2D(32, (3, 3), strides=(1, 1), activation="relu", input_shape=X.shape[1:])) |
model.add(MaxPooling2D((2, 2))) |
model.add(Conv2D(32, (3, 3), strides=(1, 1), activation="relu")) |
model.add(MaxPooling2D((2, 2))) |
model.add(Flatten()) |
model.add(Dense(256, activation="relu")) |
model.add(Dropout(0.5)) |
model.add(Dense(3, activation="softmax")) |
# 编译模型 |
opt = Adam(lr=0.001) |
model.compile(optimizer=opt, |
loss='sparse_categorical_crossentropy', |
metrics=['accuracy']) |
# 训练模型 |
epochs = 10 |
history = model.fit(X_train, y_train, epochs=epochs, |
validation_data=(X_test, y_test)) |
# 评估模型 |
score = model.evaluate(X_test, y_test, verbose=0) |
print('Test accuracy:', score[1]) |
# 绘制训练和验证准确度 |
plt.plot(history.history['acc']) |
plt.plot(history.history['val_acc']) |
plt.title('Model accuracy') |
plt.ylabel('Accuracy') |
plt.xlabel('Epoch') |
plt.legend(['Train', 'Test'], loc='upper left') |
plt.show() |
