Python Deep Learning 简明教程

1.14.2

$ pip

$pip install theano
$pip install tensorflow
$pip install keras

$python –c “import theano: print (theano.__version__)”

In[]:

# Importing the libraries
   import numpy as np
   import matplotlib.pyplot as plt
   import pandas as pd

# Importing the database
   dataset = pd.read_csv('Churn_Modelling.csv')

In[]:
X = dataset.iloc[:, 3:13].values
Y = dataset.iloc[:, 13].values
X

Y

array([1, 0, 1, ..., 1, 1, 0], dtype = int64)

from sklearn.preprocessing import LabelEncoder, OneHotEncoder
labelencoder_X_1 = LabelEncoder()
X[:,1] = labelencoder_X_1.fit_transform(X[:,1])
labelencoder_X_2 = LabelEncoder()
X[:, 2] = labelencoder_X_2.fit_transform(X[:, 2])
X

onehotencoder = OneHotEncoder(categorical features = [1])
X = onehotencoder.fit_transform(X).toarray()
X = X[:, 1:]
X

#Splitting the dataset into the Training set and the Test Set
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2)

# Feature Scaling

fromsklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)

# Importing the Keras libraries and packages
import keras
from keras.models import Sequential
from keras.layers import Dense

#Initializing Neural Network
classifier = Sequential()

classifier.add(Dense(units = 6, kernel_initializer = 'uniform',
activation = 'relu', input_dim = 11))

classifier.add(Dense(units = 6, kernel_initializer = 'uniform',
activation = 'relu'))

classifier.add(Dense(units = 1, kernel_initializer = 'uniform',
activation = 'sigmoid'))

# Compiling Neural Network
classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])

classifier.fit(X_train, y_train, batch_size = 10, epochs = 50)

# Predicting the Test set results
y_pred = classifier.predict(X_test)
y_pred = (y_pred > 0.5)

# Predicting a single new observation
"""Our goal is to predict if the customer with the following data will leave the bank:
Geography: Spain
Credit Score: 500
Gender: Female
Age: 40
Tenure: 3
Balance: 50000
Number of Products: 2
Has Credit Card: Yes
Is Active Member: Yes

# Predicting the Test set results
y_pred = classifier.predict(X_test)
y_pred = (y_pred > 0.5)

new_prediction = classifier.predict(sc.transform
(np.array([[0.0, 0, 500, 1, 40, 3, 50000, 2, 1, 1, 40000]])))
new_prediction = (new_prediction > 0.5)

from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_pred)
print (cm)

loss: 0.3384 acc: 0.8605
[ [1541 54]
[230 175] ]

Accuracy = 1541+175/2000=0.858

import numpy as np
input_data = np.array([-1, 2])
weights = {
   'node_0': np.array([3, 3]),
   'node_1': np.array([1, 5]),
   'output': np.array([2, -1])
}
node_0_input = (input_data * weights['node_0']).sum()
node_0_output = np.tanh(node_0_input)
node_1_input = (input_data * weights['node_1']).sum()
node_1_output = np.tanh(node_1_input)
hidden_layer_output = np.array(node_0_output, node_1_output)
output =(hidden_layer_output * weights['output']).sum()
print(output)

def relu(input):
   '''Define your relu activation function here'''
   # Calculate the value for the output of the relu function: output
   output = max(input,0)
      # Return the value just calculated
   return(output)
# Calculate node 0 value: node_0_output
node_0_input = (input_data * weights['node_0']).sum()
node_0_output = relu(node_0_input)

# Calculate node 1 value: node_1_output
node_1_input = (input_data * weights['node_1']).sum()
node_1_output = relu(node_1_input)

# Put node values into array: hidden_layer_outputs
hidden_layer_outputs = np.array([node_0_output, node_1_output])

# Calculate model output (do not apply relu)
odel_output = (hidden_layer_outputs * weights['output']).sum()
print(model_output)# Print model output

# Define predict_with_network()
def predict_with_network(input_data_row, weights):
   # Calculate node 0 value
   node_0_input = (input_data_row * weights['node_0']).sum()
   node_0_output = relu(node_0_input)

   # Calculate node 1 value
   node_1_input = (input_data_row * weights['node_1']).sum()
   node_1_output = relu(node_1_input)

   # Put node values into array: hidden_layer_outputs
   hidden_layer_outputs = np.array([node_0_output, node_1_output])

   # Calculate model output
   input_to_final_layer = (hidden_layer_outputs*weights['output']).sum()
   model_output = relu(input_to_final_layer)
# Return model output
   return(model_output)

# Create empty list to store prediction results
results = []
for input_data_row in input_data:
   # Append prediction to results
   results.append(predict_with_network(input_data_row, weights))
print(results)# Print results

import numpy as np
input_data = np.array([3, 5])
weights = {
   'node_0_0': np.array([2, 4]),
   'node_0_1': np.array([4, -5]),
   'node_1_0': np.array([-1, 1]),
   'node_1_1': np.array([2, 2]),
   'output': np.array([2, 7])
}
def predict_with_network(input_data):
   # Calculate node 0 in the first hidden layer
   node_0_0_input = (input_data * weights['node_0_0']).sum()
   node_0_0_output = relu(node_0_0_input)

   # Calculate node 1 in the first hidden layer
   node_0_1_input = (input_data*weights['node_0_1']).sum()
   node_0_1_output = relu(node_0_1_input)

   # Put node values into array: hidden_0_outputs
   hidden_0_outputs = np.array([node_0_0_output, node_0_1_output])

   # Calculate node 0 in the second hidden layer
   node_1_0_input = (hidden_0_outputs*weights['node_1_0']).sum()
   node_1_0_output = relu(node_1_0_input)

   # Calculate node 1 in the second hidden layer
   node_1_1_input = (hidden_0_outputs*weights['node_1_1']).sum()
   node_1_1_output = relu(node_1_1_input)

   # Put node values into array: hidden_1_outputs
   hidden_1_outputs = np.array([node_1_0_output, node_1_1_output])

   # Calculate model output: model_output
   model_output = (hidden_1_outputs*weights['output']).sum()
      # Return model_output
   return(model_output)
output = predict_with_network(input_data)
print(output)