前言

在第3篇教程里面，我們所編寫的CNN進行分類的模型準確度達到了80%趋翻。對于一個分類模型來說，80%的準確率不算很低了盒蟆。但是，在現(xiàn)有的情況下师骗，我們應該如何優(yōu)化這個模型呢历等？
在從零開始機器學習的系列里面，理論上的優(yōu)化模型可以修改超參數(shù)辟癌。同樣寒屯，在Keras的這個CNN程序中，我們可以指定其他的優(yōu)化器（這里用的是ADAM）黍少。修改卷積核大小寡夹、步長、修改激活函數(shù)的類型厂置、加入/取消全連接層菩掏、修改每個層有多少神經(jīng)元也是可行的方法。
面對這么多可以修改的地方昵济，修改現(xiàn)有的程序是必定需要做的智绸。

修改現(xiàn)有的程序

首先確定要修改的內容。全連接的層數(shù)访忿、層的大小和卷積層的層數(shù)瞧栗。
全連接層可以有0、1海铆、2層迹恐；卷積層可以有1、2卧斟、3或更多層殴边；然后就是每層的大小可以是32憎茂、64或128...
通過以下的代碼來確定這些排列組合可以得到多少模型：

dense_layers = [0, 1, 2]
layer_sizes = [32, 64, 128]
conv_layers = [1, 2, 3]

for dense_layer in dense_layers:
    for layer_size in layer_sizes:
        for conv_layer in conv_layers:
            NAME = "{}-conv-{}-nodes-{}-dense-{}".format(conv_layer, layer_size, dense_layer, int(time.time()))
            print(NAME)

運行上述Python代碼，在控制臺的輸出如下：

1-conv-32-nodes-0-dense-1540975261
2-conv-32-nodes-0-dense-1540975261
3-conv-32-nodes-0-dense-1540975261
1-conv-64-nodes-0-dense-1540975261
2-conv-64-nodes-0-dense-1540975261
3-conv-64-nodes-0-dense-1540975261
1-conv-128-nodes-0-dense-1540975261
2-conv-128-nodes-0-dense-1540975261
3-conv-128-nodes-0-dense-1540975261
1-conv-32-nodes-1-dense-1540975261
2-conv-32-nodes-1-dense-1540975261
3-conv-32-nodes-1-dense-1540975261
1-conv-64-nodes-1-dense-1540975261
2-conv-64-nodes-1-dense-1540975261
3-conv-64-nodes-1-dense-1540975261
1-conv-128-nodes-1-dense-1540975261
2-conv-128-nodes-1-dense-1540975261
3-conv-128-nodes-1-dense-1540975261
1-conv-32-nodes-2-dense-1540975261
2-conv-32-nodes-2-dense-1540975261
3-conv-32-nodes-2-dense-1540975261
1-conv-64-nodes-2-dense-1540975261
2-conv-64-nodes-2-dense-1540975261
3-conv-64-nodes-2-dense-1540975261
1-conv-128-nodes-2-dense-1540975261
2-conv-128-nodes-2-dense-1540975261
3-conv-128-nodes-2-dense-1540975261

也就是說找都，通過排列組合可以得到27種模型唇辨。
修改第四講的代碼，將上述代碼加入程序能耻，得到如下：

import time
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten, Conv2D, MaxPooling2D
import pickle
from tensorflow.keras.callbacks import TensorBoard
import time

gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.33)
sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options))

X = pickle.load(open("X.pickle","rb"))
y = pickle.load(open("y.pickle","rb"))

# normalize data image in grayscale is from 0-255
X = X/255.0

# train multiple models
dense_layers = [0, 1, 2]
layer_sizes = [32, 64, 128]
conv_layers = [1, 2, 3]

for dense_layer in dense_layers:
    for layer_size in layer_sizes:
        for conv_layer in conv_layers:
            model_name = "{}-conv-{}-nodes-{}-dense-{}".format(conv_layer, layer_size, dense_layer, int(time.time()))
            tensorboard = TensorBoard(log_dir='logs/{}'.format(model_name))
            print(model_name)
            model = Sequential()

            model.add(Conv2D(layer_size, (3, 3), input_shape = X.shape[1:]))
            model.add(Activation("relu"))
            model.add(MaxPooling2D(pool_size=(2,2)))

            for l in range(conv_layer-1):
                model.add(Conv2D(layer_size, (3, 3)))
                model.add(Activation("relu"))
                model.add(MaxPooling2D(pool_size=(2,2)))

            model.add(Flatten()) #Conv Layer是2D赏枚， DenseLayer是1D的 所以需要將ConvLayer壓平
            for l in range(dense_layer):
                model.add(Dense(layer_size))
                model.add(Activation("relu"))

            model.add(Dense(1))
            model.add(Activation("sigmoid"))

            model.compile(loss="binary_crossentropy",
                         optimizer="adam",
                         metrics=["accuracy"]) # 可以使用categorical_crossentropy作為損失函數(shù)

            model.fit(X, y, batch_size =32, epochs=20, validation_split=0.1, callbacks=[tensorboard])

在控制臺運行，一共需要訓練27個模型晓猛，因此在訓練之前饿幅，最好將logs的目錄下已經(jīng)存在的日志清空。這樣會方便我們觀察這一講中的不同參數(shù)對于模型的影響戒职。

使用TensorBoard觀察模型

和上一講一樣栗恩，使用tensorboard --logdir=logs/打開TensorBoard。

TensorBoard觀察所有模型

放大折線圖