利用卷积神经网络类AlexNet实现猫狗分类识别(图片数据增强→保存h5模型)

设计思路

处理过程及结果呈现

Found 17500 images belonging to 2 classes.

Found 7500 images belonging to 2 classes.

_________________________________________________________________

Layer (type)                 Output Shape              Param #  

=================================================================

input_1 (InputLayer)         (None, 150, 150, 3)       0        

_________________________________________________________________

conv2d_1 (Conv2D)            (None, 148, 148, 64)      1792      

_________________________________________________________________

batch_normalization_1 (Batch (None, 148, 148, 64)      256      

_________________________________________________________________

activation_1 (Activation)    (None, 148, 148, 64)      0        

_________________________________________________________________

max_pooling2d_1 (MaxPooling2 (None, 74, 74, 64)        0        

_________________________________________________________________

conv2d_2 (Conv2D)            (None, 72, 72, 64)        36928    

_________________________________________________________________

batch_normalization_2 (Batch (None, 72, 72, 64)        256      

_________________________________________________________________

activation_2 (Activation)    (None, 72, 72, 64)        0        

_________________________________________________________________

max_pooling2d_2 (MaxPooling2 (None, 36, 36, 64)        0        

_________________________________________________________________

conv2d_3 (Conv2D)            (None, 34, 34, 128)       73856    

_________________________________________________________________

batch_normalization_3 (Batch (None, 34, 34, 128)       512      

_________________________________________________________________

activation_3 (Activation)    (None, 34, 34, 128)       0        

_________________________________________________________________

max_pooling2d_3 (MaxPooling2 (None, 17, 17, 128)       0        

_________________________________________________________________

conv2d_4 (Conv2D)            (None, 15, 15, 128)       147584    

_________________________________________________________________

batch_normalization_4 (Batch (None, 15, 15, 128)       512      

_________________________________________________________________

activation_4 (Activation)    (None, 15, 15, 128)       0        

_________________________________________________________________

max_pooling2d_4 (MaxPooling2 (None, 7, 7, 128)         0        

_________________________________________________________________

flatten_1 (Flatten)          (None, 6272)              0        

_________________________________________________________________

dense_1 (Dense)              (None, 64)                401472    

_________________________________________________________________

batch_normalization_5 (Batch (None, 64)                256      

_________________________________________________________________

activation_5 (Activation)    (None, 64)                0        

_________________________________________________________________

dropout_1 (Dropout)          (None, 64)                0        

_________________________________________________________________

dense_2 (Dense)              (None, 1)                 65        

_________________________________________________________________

activation_6 (Activation)    (None, 1)                 0        

=================================================================

Total params: 663,489

Trainable params: 662,593

Non-trainable params: 896

_________________________________________________________________

None

Epoch 1/10

- 837s - loss: 0.8109 - binary_accuracy: 0.5731 - val_loss: 0.7552 - val_binary_accuracy: 0.6275

Epoch 2/10

- 972s - loss: 0.6892 - binary_accuracy: 0.6184 - val_loss: 0.6323 - val_binary_accuracy: 0.6538

Epoch 3/10

- 888s - loss: 0.6773 - binary_accuracy: 0.6275 - val_loss: 0.6702 - val_binary_accuracy: 0.6475

Epoch 4/10

- 827s - loss: 0.6503 - binary_accuracy: 0.6522 - val_loss: 1.4757 - val_binary_accuracy: 0.5437

Epoch 5/10

- 775s - loss: 0.6024 - binary_accuracy: 0.6749 - val_loss: 0.5872 - val_binary_accuracy: 0.6975

Epoch 6/10

- 775s - loss: 0.5855 - binary_accuracy: 0.6935 - val_loss: 1.6343 - val_binary_accuracy: 0.5075

Epoch 7/10

- 781s - loss: 0.5725 - binary_accuracy: 0.7117 - val_loss: 1.0417 - val_binary_accuracy: 0.5850

Epoch 8/10

- 770s - loss: 0.5594 - binary_accuracy: 0.7268 - val_loss: 0.6793 - val_binary_accuracy: 0.6150

Epoch 9/10

- 774s - loss: 0.5619 - binary_accuracy: 0.7239 - val_loss: 0.7271 - val_binary_accuracy: 0.5737

Epoch 10/10

- 772s - loss: 0.5206 - binary_accuracy: 0.7485 - val_loss: 1.2269 - val_binary_accuracy: 0.5564

train_history.history {'val_loss': [0.7552271389961243, 0.6323019933700561, 0.6702361726760864, 1.4756725096702576, 0.5872411811351776, 1.6343200182914734, 1.0417238283157348, 0.679338448047638, 0.7270535206794739, 1.2268943945566813], 'val_binary_accuracy': [0.6275, 0.65375, 0.6475, 0.54375, 0.6975, 0.5075, 0.585, 0.615, 0.57375, 0.5564102564102564], 'loss': [0.8109277236846185, 0.6891729639422509, 0.6772915293132106, 0.6502932430275025, 0.6023876513204267, 0.5855168705025027, 0.5725259766463311, 0.5594036031153894, 0.561434359863551, 0.5205760602989504], 'binary_accuracy': [0.5730846774193549, 0.6184475806451613, 0.6275201612903226, 0.6522177419354839, 0.6748991935483871, 0.6935483870967742, 0.7116935483870968, 0.7268145161290323, 0.7242424240015974, 0.7484879032258065]}