用 Keras 编写你的第一个人工神经网络

简单地训练一个四层全连接网络。

Ref: http://machinelearningmastery.com/tutorial-first-neural-network-python-keras/

 

1. Load Data

from keras.models import Sequential from keras.layers import Dense import numpy
# fix random seed for reproducibility
seed = 7 numpy.random.seed(seed) # 1.播了个随机种子 # load pima indians dataset
dataset = ("pima-indians-diabetes.csv", delimiter=",") # split into input (X) and output (Y) variables
X = dataset[:,0:8] Y = dataset[:,8]
# 2.读取了数据放在了二维数组

 

2. Define Model

• • 参数1：一层网络的结点个数【第一层（input） 8个 --> 第二层 12个 --> 第三层 8个 --> 第四层（output） 1个】
  • 参数2：初始化方法【0-0.05均匀分布】
  • 参数3：激活函数【第二、三层：rectifier (‘relu‘)；： sigmoid】

 

对应的代码，如下：

# create model
model = Sequential()
 model.add(Dense(12, input_dim=8, init='uniform', activation='relu')) model.add(Dense(8, init='uniform', activation='relu')) model.add(Dense(1, init='uniform', activation='sigmoid'))

 

3. Compile Model

• • 损失函数：logarithmic loss
  • 逼近模型：efficient gradient descent algorithm

 

对应的代码，如下：

# Compile model
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])

Finally, because it is a classification problem, we will collect and report the classification accuracy as the metric.

 

Otherwise，见<6. load model>，也可导入已有模型，继续训练。

 

4. Fit Model 

• • 循环次数：epoch
  • 批统计量：batch

 

对应的代码，如下：

# Fit the model
history_callback = model.fit(X, Y, nb_epoch=150, batch_size=10)

 

5. Evaluate Model

训练后，使用model.evaluate(...)预测成功率统计：  

# evaluate the model
scores = model.evaluate(X, Y) print("%s: %.2f%%" % (model.metrics_names[1], scores[1]*100))

 

运行结果：acc: 78.91%

Epoch 1/150
768/768 [==============================] - 0s - loss: 0.6826 - acc: 0.6328     
Epoch 2/150
768/768 [==============================] - 0s - loss: 0.6590 - acc: 0.6510     
Epoch 3/150
768/768 [==============================] - 0s - loss: 0.6475 - acc: 0.6549     
Epoch 4/150
768/768 [==============================] - 0s - loss: 0.6416 - acc: 0.6615     
Epoch 5/150
768/768 [==============================] - 0s - loss: 0.6216 - acc: 0.6745     
Epoch 6/150
768/768 [==============================] - 0s - loss: 0.6128 - acc: 0.6680     
Epoch 7/150
768/768 [==============================] - 0s - loss: 0.6018 - acc: 0.6927     
Epoch 8/150
768/768 [==============================] - 0s - loss: 0.5962 - acc: 0.6927     
Epoch 9/150
768/768 [==============================] - 0s - loss: 0.5991 - acc: 0.6953     
Epoch 10/150
768/768 [==============================] - 0s - loss: 0.5920 - acc: 0.6927     
Epoch 11/150
768/768 [==============================] - 0s - loss: 0.5905 - acc: 0.6979     
Epoch 12/150
768/768 [==============================] - 0s - loss: 0.5883 - acc: 0.6901     
Epoch 13/150
768/768 [==============================] - 0s - loss: 0.5870 - acc: 0.6953     
Epoch 14/150
768/768 [==============================] - 0s - loss: 0.5869 - acc: 0.6836     
Epoch 15/150
768/768 [==============================] - 0s - loss: 0.5815 - acc: 0.6953     
Epoch 16/150
768/768 [==============================] - 0s - loss: 0.5779 - acc: 0.6966     
Epoch 17/150
768/768 [==============================] - 0s - loss: 0.5809 - acc: 0.6849     
Epoch 18/150
768/768 [==============================] - 0s - loss: 0.5818 - acc: 0.6953     
Epoch 19/150
768/768 [==============================] - 0s - loss: 0.5814 - acc: 0.6901     
Epoch 20/150
768/768 [==============================] - 0s - loss: 0.5748 - acc: 0.7096     
Epoch 21/150
768/768 [==============================] - 0s - loss: 0.5758 - acc: 0.7005     
Epoch 22/150
768/768 [==============================] - 0s - loss: 0.5739 - acc: 0.7135     
Epoch 23/150
768/768 [==============================] - 0s - loss: 0.5736 - acc: 0.6927     
Epoch 24/150
768/768 [==============================] - 0s - loss: 0.5750 - acc: 0.6940     
Epoch 25/150
768/768 [==============================] - 0s - loss: 0.5734 - acc: 0.7031     
Epoch 26/150
768/768 [==============================] - 0s - loss: 0.5683 - acc: 0.7083     
Epoch 27/150
768/768 [==============================] - 0s - loss: 0.5688 - acc: 0.7018     
Epoch 28/150
768/768 [==============================] - 0s - loss: 0.5714 - acc: 0.7070     
Epoch 29/150
768/768 [==============================] - 0s - loss: 0.5621 - acc: 0.7188     
Epoch 30/150
768/768 [==============================] - 0s - loss: 0.5647 - acc: 0.7122     
Epoch 31/150
768/768 [==============================] - 0s - loss: 0.5630 - acc: 0.7135     
Epoch 32/150
768/768 [==============================] - 0s - loss: 0.5613 - acc: 0.7214     
Epoch 33/150
768/768 [==============================] - 0s - loss: 0.5594 - acc: 0.7188     
Epoch 34/150
768/768 [==============================] - 0s - loss: 0.5598 - acc: 0.7187     
Epoch 35/150
768/768 [==============================] - 0s - loss: 0.5624 - acc: 0.7187     
Epoch 36/150
768/768 [==============================] - 0s - loss: 0.5615 - acc: 0.7201     
Epoch 37/150
768/768 [==============================] - 0s - loss: 0.5544 - acc: 0.7214     
Epoch 38/150
768/768 [==============================] - 0s - loss: 0.5529 - acc: 0.7135     
Epoch 39/150
768/768 [==============================] - 0s - loss: 0.5550 - acc: 0.7227     
Epoch 40/150
768/768 [==============================] - 0s - loss: 0.5574 - acc: 0.7331     
Epoch 41/150
768/768 [==============================] - 0s - loss: 0.5561 - acc: 0.7357     
Epoch 42/150
768/768 [==============================] - 0s - loss: 0.5459 - acc: 0.7370     
Epoch 43/150
768/768 [==============================] - 0s - loss: 0.5481 - acc: 0.7240     
Epoch 44/150
768/768 [==============================] - 0s - loss: 0.5409 - acc: 0.7331     
Epoch 45/150
768/768 [==============================] - 0s - loss: 0.5438 - acc: 0.7422     
Epoch 46/150
768/768 [==============================] - 0s - loss: 0.5360 - acc: 0.7344     
Epoch 47/150
768/768 [==============================] - 0s - loss: 0.5393 - acc: 0.7357     
Epoch 48/150
768/768 [==============================] - 0s - loss: 0.5360 - acc: 0.7435     
Epoch 49/150
768/768 [==============================] - 0s - loss: 0.5407 - acc: 0.7370     
Epoch 50/150
768/768 [==============================] - 0s - loss: 0.5473 - acc: 0.7344     
Epoch 51/150
768/768 [==============================] - 0s - loss: 0.5287 - acc: 0.7448     
Epoch 52/150
768/768 [==============================] - 0s - loss: 0.5283 - acc: 0.7539     
Epoch 53/150
768/768 [==============================] - 0s - loss: 0.5308 - acc: 0.7396     
Epoch 54/150
768/768 [==============================] - 0s - loss: 0.5274 - acc: 0.7448     
Epoch 55/150
768/768 [==============================] - 0s - loss: 0.5241 - acc: 0.7539     
Epoch 56/150
768/768 [==============================] - 0s - loss: 0.5262 - acc: 0.7526     
Epoch 57/150
768/768 [==============================] - 0s - loss: 0.5272 - acc: 0.7422     
Epoch 58/150
768/768 [==============================] - 0s - loss: 0.5262 - acc: 0.7539     
Epoch 59/150
768/768 [==============================] - 0s - loss: 0.5224 - acc: 0.7604     
Epoch 60/150
768/768 [==============================] - 0s - loss: 0.5200 - acc: 0.7513     
Epoch 61/150
768/768 [==============================] - 0s - loss: 0.5158 - acc: 0.7578     
Epoch 62/150
768/768 [==============================] - 0s - loss: 0.5162 - acc: 0.7513     
Epoch 63/150
768/768 [==============================] - 0s - loss: 0.5097 - acc: 0.7552     
Epoch 64/150
768/768 [==============================] - 0s - loss: 0.5134 - acc: 0.7487     
Epoch 65/150
768/768 [==============================] - 0s - loss: 0.5112 - acc: 0.7435     
Epoch 66/150
768/768 [==============================] - 0s - loss: 0.5141 - acc: 0.7656     
Epoch 67/150
768/768 [==============================] - 0s - loss: 0.5082 - acc: 0.7539     
Epoch 68/150
768/768 [==============================] - 0s - loss: 0.5101 - acc: 0.7643     
Epoch 69/150
768/768 [==============================] - 0s - loss: 0.5136 - acc: 0.7409     
Epoch 70/150
768/768 [==============================] - 0s - loss: 0.5182 - acc: 0.7474     
Epoch 71/150
768/768 [==============================] - 0s - loss: 0.5185 - acc: 0.7370     
Epoch 72/150
768/768 [==============================] - 0s - loss: 0.5073 - acc: 0.7539     
Epoch 73/150
768/768 [==============================] - 0s - loss: 0.4982 - acc: 0.7682     
Epoch 74/150
768/768 [==============================] - 0s - loss: 0.4967 - acc: 0.7591     
Epoch 75/150
768/768 [==============================] - 0s - loss: 0.5070 - acc: 0.7617     
Epoch 76/150
768/768 [==============================] - 0s - loss: 0.5025 - acc: 0.7526     
Epoch 77/150
768/768 [==============================] - 0s - loss: 0.4991 - acc: 0.7604     
Epoch 78/150
768/768 [==============================] - 0s - loss: 0.4923 - acc: 0.7656     
Epoch 79/150
768/768 [==============================] - 0s - loss: 0.4998 - acc: 0.7695     
Epoch 80/150
768/768 [==============================] - 0s - loss: 0.5004 - acc: 0.7526     
Epoch 81/150
768/768 [==============================] - 0s - loss: 0.5043 - acc: 0.7552     
Epoch 82/150
768/768 [==============================] - 0s - loss: 0.5002 - acc: 0.7656     
Epoch 83/150
768/768 [==============================] - 0s - loss: 0.4932 - acc: 0.7617     
Epoch 84/150
768/768 [==============================] - 0s - loss: 0.4971 - acc: 0.7604     
Epoch 85/150
768/768 [==============================] - 0s - loss: 0.5007 - acc: 0.7513     
Epoch 86/150
768/768 [==============================] - 0s - loss: 0.4889 - acc: 0.7656     
Epoch 87/150
768/768 [==============================] - 0s - loss: 0.4953 - acc: 0.7591     
Epoch 88/150
768/768 [==============================] - 0s - loss: 0.4910 - acc: 0.7669     
Epoch 89/150
768/768 [==============================] - 0s - loss: 0.4897 - acc: 0.7604     
Epoch 90/150
768/768 [==============================] - 0s - loss: 0.4867 - acc: 0.7643     
Epoch 91/150
768/768 [==============================] - 0s - loss: 0.4915 - acc: 0.7669     
Epoch 92/150
768/768 [==============================] - 0s - loss: 0.4907 - acc: 0.7630     
Epoch 93/150
768/768 [==============================] - 0s - loss: 0.4912 - acc: 0.7604     
Epoch 94/150
768/768 [==============================] - 0s - loss: 0.4851 - acc: 0.7630     
Epoch 95/150
768/768 [==============================] - 0s - loss: 0.4821 - acc: 0.7682     
Epoch 96/150
768/768 [==============================] - 0s - loss: 0.4835 - acc: 0.7669     
Epoch 97/150
768/768 [==============================] - 0s - loss: 0.4738 - acc: 0.7773     
Epoch 98/150
768/768 [==============================] - 0s - loss: 0.5008 - acc: 0.7474     
Epoch 99/150
768/768 [==============================] - 0s - loss: 0.4841 - acc: 0.7682     
Epoch 100/150
768/768 [==============================] - 0s - loss: 0.4816 - acc: 0.7669     
Epoch 101/150
768/768 [==============================] - 0s - loss: 0.4843 - acc: 0.7695     
Epoch 102/150
768/768 [==============================] - 0s - loss: 0.4753 - acc: 0.7891     
Epoch 103/150
768/768 [==============================] - 0s - loss: 0.4841 - acc: 0.7630     
Epoch 104/150
768/768 [==============================] - 0s - loss: 0.4836 - acc: 0.7786     
Epoch 105/150
768/768 [==============================] - 0s - loss: 0.4809 - acc: 0.7708     
Epoch 106/150
768/768 [==============================] - 0s - loss: 0.4792 - acc: 0.7786     
Epoch 107/150
768/768 [==============================] - 0s - loss: 0.4831 - acc: 0.7734     
Epoch 108/150
768/768 [==============================] - 0s - loss: 0.4783 - acc: 0.7852     
Epoch 109/150
768/768 [==============================] - 0s - loss: 0.4784 - acc: 0.7708     
Epoch 110/150
768/768 [==============================] - 0s - loss: 0.4803 - acc: 0.7682     
Epoch 111/150
768/768 [==============================] - 0s - loss: 0.4704 - acc: 0.7734     
Epoch 112/150
768/768 [==============================] - 0s - loss: 0.4752 - acc: 0.7878     
Epoch 113/150
768/768 [==============================] - 0s - loss: 0.4776 - acc: 0.7760     
Epoch 114/150
768/768 [==============================] - 0s - loss: 0.4849 - acc: 0.7604     
Epoch 115/150
768/768 [==============================] - 0s - loss: 0.4773 - acc: 0.7682     
Epoch 116/150
768/768 [==============================] - 0s - loss: 0.4712 - acc: 0.7773     
Epoch 117/150
768/768 [==============================] - 0s - loss: 0.4675 - acc: 0.7786     
Epoch 118/150
768/768 [==============================] - 0s - loss: 0.4660 - acc: 0.7839     
Epoch 119/150
768/768 [==============================] - 0s - loss: 0.4702 - acc: 0.7891     
Epoch 120/150
768/768 [==============================] - 0s - loss: 0.4699 - acc: 0.7852     
Epoch 121/150
768/768 [==============================] - 0s - loss: 0.4786 - acc: 0.7852     
Epoch 122/150
768/768 [==============================] - 0s - loss: 0.4745 - acc: 0.7786     
Epoch 123/150
768/768 [==============================] - 0s - loss: 0.4684 - acc: 0.7839     
Epoch 124/150
768/768 [==============================] - 0s - loss: 0.4709 - acc: 0.7760     
Epoch 125/150
768/768 [==============================] - 0s - loss: 0.4699 - acc: 0.7747     
Epoch 126/150
768/768 [==============================] - 0s - loss: 0.4649 - acc: 0.7747     
Epoch 127/150
768/768 [==============================] - 0s - loss: 0.4709 - acc: 0.7708     
Epoch 128/150
768/768 [==============================] - 0s - loss: 0.4573 - acc: 0.7982     
Epoch 129/150
768/768 [==============================] - 0s - loss: 0.4646 - acc: 0.7943     
Epoch 130/150
768/768 [==============================] - 0s - loss: 0.4775 - acc: 0.7773     
Epoch 131/150
768/768 [==============================] - 0s - loss: 0.4613 - acc: 0.7799     
Epoch 132/150
768/768 [==============================] - 0s - loss: 0.4608 - acc: 0.7799     
Epoch 133/150
768/768 [==============================] - 0s - loss: 0.4737 - acc: 0.7826     
Epoch 134/150
768/768 [==============================] - 0s - loss: 0.4711 - acc: 0.7773     
Epoch 135/150
768/768 [==============================] - 0s - loss: 0.4665 - acc: 0.7839     
Epoch 136/150
768/768 [==============================] - 0s - loss: 0.4579 - acc: 0.7969     
Epoch 137/150
768/768 [==============================] - 0s - loss: 0.4621 - acc: 0.7917     
Epoch 138/150
768/768 [==============================] - 0s - loss: 0.4684 - acc: 0.7760     
Epoch 139/150
768/768 [==============================] - 0s - loss: 0.4597 - acc: 0.7839     
Epoch 140/150
768/768 [==============================] - 0s - loss: 0.4593 - acc: 0.7799     
Epoch 141/150
768/768 [==============================] - 0s - loss: 0.4624 - acc: 0.7799     
Epoch 142/150
768/768 [==============================] - 0s - loss: 0.4609 - acc: 0.7786     
Epoch 143/150
768/768 [==============================] - 0s - loss: 0.4648 - acc: 0.7826     
Epoch 144/150
768/768 [==============================] - 0s - loss: 0.4541 - acc: 0.8060     
Epoch 145/150
768/768 [==============================] - 0s - loss: 0.4597 - acc: 0.7852     
Epoch 146/150
768/768 [==============================] - 0s - loss: 0.4639 - acc: 0.7891     
Epoch 147/150
768/768 [==============================] - 0s - loss: 0.4548 - acc: 0.7865     
Epoch 148/150
768/768 [==============================] - 0s - loss: 0.4659 - acc: 0.7786     
Epoch 149/150
768/768 [==============================] - 0s - loss: 0.4596 - acc: 0.7799     
Epoch 150/150
768/768 [==============================] - 0s - loss: 0.4615 - acc: 0.7773     
 32/768 [>.............................] - ETA: 0sacc: 78.91%

 

6. Save & load model

分析log：How to log Keras loss output to a file

loss_history = history_callback.history["loss"] acc_history = history_callback.history["acc"]

Save and Load Your Keras Deep Learning Models

• • 模型：model.json
  • 权重：model.h5

 

对应的代码，如下：

# serialize model to JSON
model_json = model.to_json() with open("model.json", "w+") as json_file: json_file.write(model_json)
# serialize weights to HDF5
model.save_weights("model.h5") print("Saved model to disk") # later...
 
# load json and create model
json_file = open('model.json', 'r') loaded_model_json = json_file.read() json_file.close() loaded_model = model_from_json(loaded_model_json)
# load weights into new model
loaded_model.load_weights("model.h5") print("Loaded model from disk")

 

# serialize model to YAML
model_yaml = model.to_yaml() with open("model.yaml", "w") as yaml_file: yaml_file.write(model_yaml)
# serialize weights to HDF5
model.save_weights("model.h5") print("Saved model to disk") # later...
 
# load YAML and create model
yaml_file = open('model.yaml', 'r') loaded_model_yaml = yaml_file.read() yaml_file.close() loaded_model = model_from_yaml(loaded_model_yaml)
# load weights into new model
loaded_model.load_weights("model.h5") print("Loaded model from disk")

  

7. Make Predictions

通过numpy.loadtxt(...) 获取新的数据，放入X中。

# calculate predictions
predictions = model.predict(X) # round predictions
rounded = [int(round(x[0])) for x in predictions] print(rounded)