Tensorflow: Save and Load Model to retrain

 This demo program show how to save a trained model and then load it for retraining. I splitted the original file into 2 which I used in this post "Predictive Model: Customer Subsciption (Continue/Discontinue) with Tensorflow". 

The output:

Orignal Model Accuracy result:

Loaded Model Accuracy result:

The code:

First program:

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import os import pandas as pd from sklearn.model_selection import train_test_split from tensorflow.keras.models import Sequential, load_model from tensorflow.keras.layers import Dense, Flatten from sklearn.metrics import accuracy_score from sklearn.metrics import ConfusionMatrixDisplay, classification_report, confusion_matrix from matplotlib import style import tensorflow as tf from tensorflow import keras style.use('classic') df = pd.read_csv('cont_subs.csv') X = pd.get_dummies(df.drop(['cont_subs', 'Customer ID'], axis=1)) y = df['cont_subs'].apply(lambda x: 1 if x=='Yes' else 0) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.15) X.columns y_train.head() model = Sequential() model.add(Dense(units=64, activation='relu', input_dim=len(X_train.columns))) model.add(Dense(units=128, activation='relu')) model.add(Dense(units=128, activation='relu')) model.add(Dense(units=1, activation='sigmoid')) checkpoint_path = "training_1/cp.ckpt" checkpoint_dir = os.path.dirname(checkpoint_path) # Create a callback that saves the model's weights cp_callback = tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_path, save_weights_only=True, verbose=1) model.compile(loss='binary_crossentropy', optimizer='sgd', metrics='accuracy') model.fit(X_train, y_train, epochs=256, batch_size=32, callbacks=[cp_callback]) y_hat = model.predict(X_test) y_hat = [0 if val < 0.5 else 1 for val in y_hat] print('Model Accuracy' + str(accuracy_score(y_test, y_hat))) # Display the model's architecture model.summary() cm = confusion_matrix(y_test, y_hat) disp = ConfusionMatrixDisplay(confusion_matrix = cm) disp.plot() model.save('my_model')

 

The Loaded Model Program:

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import os import pandas as pd from sklearn.model_selection import train_test_split from tensorflow.keras.models import Sequential, load_model from tensorflow.keras.layers import Dense, Flatten from sklearn.metrics import accuracy_score from sklearn.metrics import ConfusionMatrixDisplay, classification_report, confusion_matrix from matplotlib import style import tensorflow as tf from tensorflow import keras style.use('classic') df = pd.read_csv('cont_subs1.csv') X = pd.get_dummies(df.drop(['cont_subs', 'Customer ID'], axis=1)) y = df['cont_subs'].apply(lambda x: 1 if x=='Yes' else 0) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.15) X.columns y_train.head() new_model = tf.keras.models.load_model('my_model') # Check its architecture new_model.summary() new_model = Sequential() new_model.add(Dense(units=64, activation='relu', input_dim=len(X_train.columns))) new_model.add(Dense(units=128, activation='relu')) new_model.add(Dense(units=1, activation='sigmoid')) checkpoint_path = "training_1/cp.ckpt" checkpoint_dir = os.path.dirname(checkpoint_path) # Create a callback that saves the model's weights cp_callback = tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_path, save_weights_only=True, verbose=1) new_model.compile(loss='binary_crossentropy', optimizer='sgd', metrics='accuracy') new_model.fit(X_train, y_train, epochs=256, batch_size=32, callbacks=[cp_callback]) new_model.evaluate(X_train, y_train, verbose=2) y_hat = new_model.predict(X_test) y_hat = [0 if val < 0.5 else 1 for val in y_hat] #accuracy_score(y_test, y_hat) print('Loaded Model Accuracy' + str(accuracy_score(y_test, y_hat))) model.save('my_model') new_model.summary()