使用深度学习检测疟疾

基于这些代码的架构，我们的 CNN 模型有三个卷积和一个池化层，其后是两个致密层，以及用于正则化的失活。让我们训练我们的模型。

import datetime
 
logdir = os.path.join('/home/dipanzan_sarkar/projects/tensorboard_logs', 
                      datetime.datetime.now().strftime("%Y%m%d-%H%M%S"))
tensorboard_callback = tf.keras.callbacks.TensorBoard(logdir, histogram_freq=1)
reduce_lr = tf.keras.callbacks.ReduceLROnPlateau(monitor='val_loss', factor=0.5,
                              patience=2, min_lr=0.000001)
callbacks = [reduce_lr, tensorboard_callback]
 
history = model.fit(x=train_imgs_scaled, y=train_labels_enc, 
                    batch_size=BATCH_SIZE,
                    epochs=EPOCHS, 
                    validation_data=(val_imgs_scaled, val_labels_enc), 
                    callbacks=callbacks,
                    verbose=1)
                    
 
# Output
Train on 17361 samples, validate on 1929 samples
Epoch 1/25
17361/17361 [====] - 32s 2ms/sample - loss: 0.4373 - accuracy: 0.7814 - val_loss: 0.1834 - val_accuracy: 0.9393
Epoch 2/25
17361/17361 [====] - 30s 2ms/sample - loss: 0.1725 - accuracy: 0.9434 - val_loss: 0.1567 - val_accuracy: 0.9513
...
...
Epoch 24/25
17361/17361 [====] - 30s 2ms/sample - loss: 0.0036 - accuracy: 0.9993 - val_loss: 0.3693 - val_accuracy: 0.9565
Epoch 25/25
17361/17361 [====] - 30s 2ms/sample - loss: 0.0034 - accuracy: 0.9994 - val_loss: 0.3699 - val_accuracy: 0.9559

我们获得了 95.6% 的验证精确率，这很好，尽管我们的模型看起来有些过拟合（通过查看我们的训练精确度，是 99.9%）。通过绘制训练和验证的精度和损失曲线，我们可以清楚地看到这一点。

f, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 4))
t = f.suptitle('Basic CNN Performance', fontsize=12)
f.subplots_adjust(top=0.85, wspace=0.3)
 
max_epoch = len(history.history['accuracy'])+1
epoch_list = list(range(1,max_epoch))
ax1.plot(epoch_list, history.history['accuracy'], label='Train Accuracy')
ax1.plot(epoch_list, history.history['val_accuracy'], label='Validation Accuracy')
ax1.set_xticks(np.arange(1, max_epoch, 5))
ax1.set_ylabel('Accuracy Value')
ax1.set_xlabel('Epoch')
ax1.set_title('Accuracy')
l1 = ax1.legend(loc="best")
 
ax2.plot(epoch_list, history.history['loss'], label='Train Loss')
ax2.plot(epoch_list, history.history['val_loss'], label='Validation Loss')
ax2.set_xticks(np.arange(1, max_epoch, 5))
ax2.set_ylabel('Loss Value')
ax2.set_xlabel('Epoch')
ax2.set_title('Loss')
l2 = ax2.legend(loc="best")

基础 CNN 学习曲线

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