目录

创建模型

热编码

keras 模型

模型测试

创建数据集

建立模型

测试

---

创建模型

"""
model = tf.keras.models.Sequential([
  tf.keras.layers.Dense(10, activation=tf.nn.relu, input_dim=1),
  tf.keras.layers.Dense(10, activation=tf.nn.relu),
  tf.keras.layers.Dense(2, activation=tf.nn.softmax)
])
"""

#!/usr/bin/env python
# -*- coding=utf-8 -*-
import tensorflow as tf
import numpy as np


x1=np.random.random((500,1))
x2=np.random.random((500,1))+1
x_train=np.concatenate((x1, x2))

y1=np.zeros((500,), dtype=int)
y2=np.ones((500,), dtype=int)
y_train=np.concatenate((y1, y2))


# 建立模型
model = tf.contrib.keras.models.Sequential()
# 加入 2D 的 Convolution Layer，接著一層 ReLU 的 Activation 函數
model.add(tf.contrib.keras.layers.Dense(units=10,
                 activation=tf.nn.relu,    #tf.nn.relu
                 input_dim=1))
model.add(tf.contrib.keras.layers.Dense(units=10,
                 activation=tf.nn.relu ))  # tf.nn.relu
model.add(tf.contrib.keras.layers.Dense(units=2,
                 activation=tf.nn.softmax ))  #tf.nn.softmax



model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

model.fit(x_train, y_train,
          epochs=20,
          batch_size=128)


#測試
x_test=np.array([[0.22],[0.31],[1.22],[1.33]])
y_test=np.array([0,0,1,1])

score = model.evaluate(x_test, y_test, batch_size=128)
print("score:",score)

predict = model.predict(x_test)
print("predict:",predict)
print("Ans:",np.argmax(predict[0]),np.argmax(predict[1]),np.argmax(predict[2]),np.argmax(predict[3]))

predict2 = model.predict_classes(x_test)
print("predict_classes:",predict2)
print("y_test",y_test[:])

热编码

"""
model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])
"""
 

#!/usr/bin/env python
# -*- coding=utf-8 -*-
import tensorflow as tf
import numpy as np


x1=np.random.random((500,1))
x2=np.random.random((500,1))+1
x_train=np.concatenate((x1, x2))

y1=np.zeros((500,), dtype=int)
y2=np.ones((500,), dtype=int)
y_train=np.concatenate((y1, y2))


# 將數字轉為 One-hot 向量
y_train2 = tf.contrib.keras.utils.to_categorical(y_train,  num_classes=2)

# 建立模型
model = tf.contrib.keras.models.Sequential()
model.add(tf.contrib.keras.layers.Dense(units=10,
    activation=tf.nn.relu,
    input_dim=1))
model.add(tf.contrib.keras.layers.Dense(units=10,
    activation=tf.nn.relu ))  # tf.nn.relu
model.add(tf.contrib.keras.layers.Dense(units=2,
    activation=tf.nn.softmax ))  #tf.nn.softmax

# 設定模型的 Loss 函數、Optimizer 以及用來判斷模型好壞的依據（metrics）
model.compile(optimizer='adam',
    loss=tf.contrib.keras.losses.categorical_crossentropy,
    metrics=['accuracy'])


model.fit(x_train, y_train2,
          epochs=20,
          batch_size=128)



#測試
x_test=np.array([[0.22],[0.31],[1.22],[1.33]])
y_test=np.array([0,0,1,1])
y_test2 = tf.contrib.keras.utils.to_categorical(y_test,  num_classes=2)

score = model.evaluate(x_test, y_test2, batch_size=128)
print("score:",score)

predict = model.predict(x_test)
print("predict:",predict)
print("Ans:",np.argmax(predict[0]),np.argmax(predict[1]),np.argmax(predict[2]),np.argmax(predict[3]))

predict2 = model.predict_classes(x_test)
print("predict_classes:",predict2)
print("y_test",y_test[:])

keras 模型

""" 
x_train = np.random.random((1000, 20))
y_train = keras.utils.to_categorical(np.random.randint(2, size=(1000, 1)), num_classes=2)
x_test = np.random.random((100, 20))
y_test = keras.utils.to_categorical(np.random.randint(2, size=(100, 1)), num_classes=2)

"""

#!/usr/bin/env python
# -*- coding=utf-8 -*-
import keras
from keras.models import Sequential
from keras.layers import Dense
#from keras.optimizers import SGD, Dropout, Activation

# Generate dummy data
import numpy as np


x1=np.random.random((500,1))
x2=np.random.random((500,1))+1
x_train=np.concatenate((x1, x2))

y1=np.zeros((500,), dtype=int)
y2=np.ones((500,), dtype=int)
y_train=np.concatenate((y1, y2))
#y_train = keras.utils.to_categorical(y_train, num_classes=2)



model = Sequential()
model.add(Dense(units=10, activation='relu', input_dim=1))
model.add(Dense(units=10, activation='relu'))
model.add(Dense(units=2, activation='softmax'))


model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

model.fit(x_train, y_train,
          epochs=20,
          batch_size=128)


#測試
x_test=np.array([[0.22],[0.31],[1.22],[1.33]])
y_test=np.array([0,0,1,1])

score = model.evaluate(x_test, y_test, batch_size=128)
print("score:",score)

predict = model.predict(x_test)
print("predict:",predict)
print("Ans:",np.argmax(predict[0]),np.argmax(predict[1]),np.argmax(predict[2]),np.argmax(predict[3]))

predict2 = model.predict_classes(x_test)
print("predict_classes:",predict2)
print("y_test",y_test[:])

模型测试

创建数据集

def CreateDatasets(high,iNum,iArraySize):

    x_train = np.random.random((iNum, iArraySize)) * float(high)

    y_train = ((x_train[:iNum,0]+x_train[:iNum,1])/2).astype(int)     # 取整數

    return x_train, y_train,tf.contrib.keras.utils.to_categorical(y_train, num_classes=(high))

category=10

dim=2

x_train,y_train,y_train2=CreateDatasets(category,1000*10,dim) # 修改這裡

建立模型

model = tf.contrib.keras.models.Sequential()

model.add(tf.contrib.keras.layers.Dense(units=10*100,

    activation=tf.nn.relu,

    input_dim=dim))

model.add(tf.contrib.keras.layers.Dense(units=10*100,

    activation=tf.nn.relu ))

model.add(tf.contrib.keras.layers.Dense(units=10*100,

    activation=tf.nn.relu ))

model.add(tf.contrib.keras.layers.Dense(units=10*100,

    activation=tf.nn.relu ))

model.add(tf.contrib.keras.layers.Dense(units=category,

    activation=tf.nn.softmax ))

model.compile(optimizer='adam',

    loss=tf.contrib.keras.losses.categorical_crossentropy,

    metrics=['accuracy'])

model.fit(x_train, y_train2,

          epochs=20*100,

          batch_size=128*100)

测试

#測試
x_test,y_test,y_test2=CreateDatasets(category,10,dim)
score = model.evaluate(x_test, y_test2, batch_size=128)
print("score:",score)

predict = model.predict(x_test)
#print("predict:",predict)
print("Ans:",np.argmax(predict[0]),np.argmax(predict[1]),np.argmax(predict[2]),np.argmax(predict[3]))

predict2 = model.predict_classes(x_test)
print("predict_classes:",predict2)
print("y_test",y_test[:])