How to save a modal and load again to use?

I have no more knowledge of python. This is my ANN modal code in python. This code contains to predict customers situation in binary output. Which is customers leave or not.

Code:

import numpy as np

import matplotlib.pyplot as plt

import pandas as pd



# Importing the dataset

dataset = pd.read_csv('Churn_Modelling.csv')

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

y = dataset.iloc[:, 13].values



# Encoding categorical data

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])

onehotencoder = OneHotEncoder(categorical_features = [1])

X = onehotencoder.fit_transform(X).toarray()

X = X[:, 1:]



# Splitting the dataset into the Training set and 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, random_state = 0)



# Feature Scaling

from sklearn.preprocessing import StandardScaler

sc = StandardScaler()

X_train = sc.fit_transform(X_train)

X_test = sc.transform(X_test)



# Part 2 - Now let's make the ANN!



# Importing the Keras libraries and packages

import keras

from keras.models import Sequential

from keras.layers import Dense



# Initialising the ANN

classifier = Sequential()



# Adding the input layer and the first hidden layer

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



# Adding the second hidden layer

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



# Adding the output layer

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



# Compiling the ANN

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



# Fitting the ANN to the Training set

classifier.fit(X_train, y_train, batch_size = 10, epochs = 100)



# Part 3 - Making predictions and evaluating the model



# Predicting the Test set results

y_pred = classifier.predict(X_test)

y_pred = (y_pred > 0.5)

I want to know how to save this modal as h5 using keras. After I saved it how to load again in another project to predict the data.

edited Jan 19 at 6:27

asked Jan 19 at 6:22

Ind

306

"m5" or ".h5"? if it's the latter, these links may help - machinelearningmastery.com/save-load-keras-deep-learning-models and jovianlin.io/saving-loading-keras-models

– gireesh4manu
Jan 19 at 6:25

This link on SO gives a more detailed explanation for the question in picture - stackoverflow.com/questions/47266383/…

– gireesh4manu
Jan 19 at 6:26

try pickle module in python..

– Ashu Grover
Jan 19 at 6:31

why pickle is good?

– Ind
Jan 19 at 6:36

Possible duplicate of How to save final model using keras?

– sdcbr
Jan 19 at 10:33

add a comment |

I have no more knowledge of python. This is my ANN modal code in python. This code contains to predict customers situation in binary output. Which is customers leave or not.

Code:

import numpy as np

import matplotlib.pyplot as plt

import pandas as pd



# Importing the dataset

dataset = pd.read_csv('Churn_Modelling.csv')

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

y = dataset.iloc[:, 13].values



# Encoding categorical data

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])

onehotencoder = OneHotEncoder(categorical_features = [1])

X = onehotencoder.fit_transform(X).toarray()

X = X[:, 1:]



# Splitting the dataset into the Training set and 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, random_state = 0)



# Feature Scaling

from sklearn.preprocessing import StandardScaler

sc = StandardScaler()

X_train = sc.fit_transform(X_train)

X_test = sc.transform(X_test)



# Part 2 - Now let's make the ANN!



# Importing the Keras libraries and packages

import keras

from keras.models import Sequential

from keras.layers import Dense



# Initialising the ANN

classifier = Sequential()



# Adding the input layer and the first hidden layer

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



# Adding the second hidden layer

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



# Adding the output layer

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



# Compiling the ANN

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



# Fitting the ANN to the Training set

classifier.fit(X_train, y_train, batch_size = 10, epochs = 100)



# Part 3 - Making predictions and evaluating the model



# Predicting the Test set results

y_pred = classifier.predict(X_test)

y_pred = (y_pred > 0.5)

I want to know how to save this modal as h5 using keras. After I saved it how to load again in another project to predict the data.

edited Jan 19 at 6:27

asked Jan 19 at 6:22

Ind

306

"m5" or ".h5"? if it's the latter, these links may help - machinelearningmastery.com/save-load-keras-deep-learning-models and jovianlin.io/saving-loading-keras-models

– gireesh4manu
Jan 19 at 6:25

This link on SO gives a more detailed explanation for the question in picture - stackoverflow.com/questions/47266383/…

– gireesh4manu
Jan 19 at 6:26

try pickle module in python..

– Ashu Grover
Jan 19 at 6:31

why pickle is good?

– Ind
Jan 19 at 6:36

Possible duplicate of How to save final model using keras?

– sdcbr
Jan 19 at 10:33

add a comment |

I have no more knowledge of python. This is my ANN modal code in python. This code contains to predict customers situation in binary output. Which is customers leave or not.

Code:

import numpy as np

import matplotlib.pyplot as plt

import pandas as pd



# Importing the dataset

dataset = pd.read_csv('Churn_Modelling.csv')

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

y = dataset.iloc[:, 13].values



# Encoding categorical data

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])

onehotencoder = OneHotEncoder(categorical_features = [1])

X = onehotencoder.fit_transform(X).toarray()

X = X[:, 1:]



# Splitting the dataset into the Training set and 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, random_state = 0)



# Feature Scaling

from sklearn.preprocessing import StandardScaler

sc = StandardScaler()

X_train = sc.fit_transform(X_train)

X_test = sc.transform(X_test)



# Part 2 - Now let's make the ANN!



# Importing the Keras libraries and packages

import keras

from keras.models import Sequential

from keras.layers import Dense



# Initialising the ANN

classifier = Sequential()



# Adding the input layer and the first hidden layer

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



# Adding the second hidden layer

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



# Adding the output layer

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



# Compiling the ANN

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



# Fitting the ANN to the Training set

classifier.fit(X_train, y_train, batch_size = 10, epochs = 100)



# Part 3 - Making predictions and evaluating the model



# Predicting the Test set results

y_pred = classifier.predict(X_test)

y_pred = (y_pred > 0.5)

I want to know how to save this modal as h5 using keras. After I saved it how to load again in another project to predict the data.

edited Jan 19 at 6:27

asked Jan 19 at 6:22

Ind

306

I have no more knowledge of python. This is my ANN modal code in python. This code contains to predict customers situation in binary output. Which is customers leave or not.

Code:

import numpy as np

import matplotlib.pyplot as plt

import pandas as pd



# Importing the dataset

dataset = pd.read_csv('Churn_Modelling.csv')

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

y = dataset.iloc[:, 13].values



# Encoding categorical data

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])

onehotencoder = OneHotEncoder(categorical_features = [1])

X = onehotencoder.fit_transform(X).toarray()

X = X[:, 1:]



# Splitting the dataset into the Training set and 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, random_state = 0)



# Feature Scaling

from sklearn.preprocessing import StandardScaler

sc = StandardScaler()

X_train = sc.fit_transform(X_train)

X_test = sc.transform(X_test)



# Part 2 - Now let's make the ANN!



# Importing the Keras libraries and packages

import keras

from keras.models import Sequential

from keras.layers import Dense



# Initialising the ANN

classifier = Sequential()



# Adding the input layer and the first hidden layer

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



# Adding the second hidden layer

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



# Adding the output layer

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



# Compiling the ANN

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



# Fitting the ANN to the Training set

classifier.fit(X_train, y_train, batch_size = 10, epochs = 100)



# Part 3 - Making predictions and evaluating the model



# Predicting the Test set results

y_pred = classifier.predict(X_test)

y_pred = (y_pred > 0.5)

I want to know how to save this modal as h5 using keras. After I saved it how to load again in another project to predict the data.

python keras

edited Jan 19 at 6:27

asked Jan 19 at 6:22

Ind

306

edited Jan 19 at 6:27

asked Jan 19 at 6:22

Ind

306

edited Jan 19 at 6:27

asked Jan 19 at 6:22

Ind

306

asked Jan 19 at 6:22

Ind

306

asked Jan 19 at 6:22

Ind

306

"m5" or ".h5"? if it's the latter, these links may help - machinelearningmastery.com/save-load-keras-deep-learning-models and jovianlin.io/saving-loading-keras-models

– gireesh4manu
Jan 19 at 6:25

This link on SO gives a more detailed explanation for the question in picture - stackoverflow.com/questions/47266383/…

– gireesh4manu
Jan 19 at 6:26

try pickle module in python..

– Ashu Grover
Jan 19 at 6:31

why pickle is good?

– Ind
Jan 19 at 6:36

Possible duplicate of How to save final model using keras?

– sdcbr
Jan 19 at 10:33

add a comment |

"m5" or ".h5"? if it's the latter, these links may help - machinelearningmastery.com/save-load-keras-deep-learning-models and jovianlin.io/saving-loading-keras-models

– gireesh4manu
Jan 19 at 6:25

This link on SO gives a more detailed explanation for the question in picture - stackoverflow.com/questions/47266383/…

– gireesh4manu
Jan 19 at 6:26

try pickle module in python..

– Ashu Grover
Jan 19 at 6:31

why pickle is good?

– Ind
Jan 19 at 6:36

Possible duplicate of How to save final model using keras?

– sdcbr
Jan 19 at 10:33

"m5" or ".h5"? if it's the latter, these links may help - machinelearningmastery.com/save-load-keras-deep-learning-models and jovianlin.io/saving-loading-keras-models

– gireesh4manu
Jan 19 at 6:25

This link on SO gives a more detailed explanation for the question in picture - stackoverflow.com/questions/47266383/…

– gireesh4manu
Jan 19 at 6:26

try pickle module in python..

– Ashu Grover
Jan 19 at 6:31

why pickle is good?

– Ind
Jan 19 at 6:36

Possible duplicate of How to save final model using keras?

– sdcbr
Jan 19 at 10:33

add a comment |

1 Answer
1

active

oldest

votes

Inorder to save the model, You can do the one below:

from keras.models import load_model

model.save('model_file.h5')

And to load the model back use:

my_model = load_model('model_file.h5')

answered Jan 19 at 6:46

RAM SHANKER G

77110

Where the model word come from?

– Ind
Jan 19 at 10:35

The model is the one you created.For example model = KerasClassifier(build_fn=baseline_model, epochs=200, batch_size=50, verbose=0)

– RAM SHANKER G
Jan 19 at 10:49

add a comment |

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1 Answer
1

active

oldest

votes

1 Answer
1

active

oldest

votes

Inorder to save the model, You can do the one below:

from keras.models import load_model

model.save('model_file.h5')

And to load the model back use:

my_model = load_model('model_file.h5')

answered Jan 19 at 6:46

RAM SHANKER G

77110

Where the model word come from?

– Ind
Jan 19 at 10:35

The model is the one you created.For example model = KerasClassifier(build_fn=baseline_model, epochs=200, batch_size=50, verbose=0)

– RAM SHANKER G
Jan 19 at 10:49

add a comment |

Inorder to save the model, You can do the one below:

from keras.models import load_model

model.save('model_file.h5')

And to load the model back use:

my_model = load_model('model_file.h5')

answered Jan 19 at 6:46

RAM SHANKER G

77110

Where the model word come from?

– Ind
Jan 19 at 10:35

The model is the one you created.For example model = KerasClassifier(build_fn=baseline_model, epochs=200, batch_size=50, verbose=0)

– RAM SHANKER G
Jan 19 at 10:49

add a comment |

Inorder to save the model, You can do the one below:

from keras.models import load_model

model.save('model_file.h5')

And to load the model back use:

my_model = load_model('model_file.h5')

answered Jan 19 at 6:46

RAM SHANKER G

77110

Inorder to save the model, You can do the one below:

from keras.models import load_model

model.save('model_file.h5')

And to load the model back use:

my_model = load_model('model_file.h5')

answered Jan 19 at 6:46

RAM SHANKER G

77110

answered Jan 19 at 6:46

RAM SHANKER G

77110

answered Jan 19 at 6:46

RAM SHANKER G

77110

answered Jan 19 at 6:46

RAM SHANKER G

77110

Where the model word come from?

– Ind
Jan 19 at 10:35

The model is the one you created.For example model = KerasClassifier(build_fn=baseline_model, epochs=200, batch_size=50, verbose=0)

– RAM SHANKER G
Jan 19 at 10:49

add a comment |

Where the model word come from?

– Ind
Jan 19 at 10:35

The model is the one you created.For example model = KerasClassifier(build_fn=baseline_model, epochs=200, batch_size=50, verbose=0)

– RAM SHANKER G
Jan 19 at 10:49

Where the model word come from?

– Ind
Jan 19 at 10:35

The model is the one you created.For example model = KerasClassifier(build_fn=baseline_model, epochs=200, batch_size=50, verbose=0)

– RAM SHANKER G
Jan 19 at 10:49

add a comment |

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