I saved the history of training by
history = model.fit(train_generator, epochs=epochs, steps_per_epoch=train_steps,
verbose=1, callbacks=callbacks, validation_data=val_generator,
validation_steps=val_steps,batch_size=16)
with open('history_epochs.pkl', 'wb') as f:
dump(history.history, f)
Can I use the file of history to continue from the last epoch? and how please
CodePudding user response：
You can use the pickle file to save and load your model and continue training:
- Create your model
- Train your model
- Save your model as a
picklefile
Code for the above steps:
import matplotlib.pyplot as plt
import tensorflow as tf
import numpy as np
import joblib
(X_train, y_train), (X_test, y_test) = tf.keras.datasets.fashion_mnist.load_data()
class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat','Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']
fig, axes = plt.subplots(2,5,figsize=(15,6))
for idx, axe in enumerate(axes.flatten()):
axe.axis('off')
idx_img = np.argwhere(y_train==idx)[0][0]
axe.imshow(X_train[idx_img], cmap=plt.cm.binary)
axe.set_title(class_names[y_train[idx_img]])
X_train = X_train.astype('float32') / 255.0
X_train = tf.expand_dims(X_train, axis=-1)
X_test = X_test.astype('float32') / 255.0
X_test = tf.expand_dims(X_test, axis=-1)
y_train = tf.keras.utils.to_categorical(y_train, 10)
y_test = tf.keras.utils.to_categorical(y_test, 10)
model = tf.keras.Sequential()
model.add(tf.keras.Input(shape=(X_train.shape[1], X_train.shape[1], 1)))
model.add(tf.keras.layers.Conv2D(128, (3,3), activation='relu'))
model.add(tf.keras.layers.BatchNormalization())
model.add(tf.keras.layers.Dropout(rate=.4))
model.add(tf.keras.layers.Conv2D(64, (3,3), activation='relu'))
model.add(tf.keras.layers.BatchNormalization())
model.add(tf.keras.layers.Dropout(rate=.4))
model.add(tf.keras.layers.Conv2D(128, (3,3), activation='relu'))
model.add(tf.keras.layers.BatchNormalization())
model.add(tf.keras.layers.Dropout(rate=.4))
model.add(tf.keras.layers.Flatten())
model.add(tf.keras.layers.Dense(512, activation='relu'))
model.add(tf.keras.layers.Dropout(rate=.4))
model.add(tf.keras.layers.Dense(128, activation='relu'))
model.add(tf.keras.layers.Dropout(rate=.4))
model.add(tf.keras.layers.Dense(10, activation='sigmoid'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
model.summary()
model.fit(X_train, y_train, batch_size=256, epochs=3, verbose=1, validation_split=.2)
model.evaluate(X_test, y_test, verbose=1)
joblib.dump(model, 'model.pkl')
Output:
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d (Conv2D) (None, 26, 26, 128) 1280
batch_normalization (BatchN (None, 26, 26, 128) 512
ormalization)
dropout (Dropout) (None, 26, 26, 128) 0
conv2d_1 (Conv2D) (None, 24, 24, 64) 73792
batch_normalization_1 (Batc (None, 24, 24, 64) 256
hNormalization)
dropout_1 (Dropout) (None, 24, 24, 64) 0
conv2d_2 (Conv2D) (None, 22, 22, 128) 73856
batch_normalization_2 (Batc (None, 22, 22, 128) 512
hNormalization)
dropout_2 (Dropout) (None, 22, 22, 128) 0
flatten (Flatten) (None, 61952) 0
dense (Dense) (None, 512) 31719936
dropout_3 (Dropout) (None, 512) 0
dense_1 (Dense) (None, 128) 65664
dropout_4 (Dropout) (None, 128) 0
dense_2 (Dense) (None, 10) 1290
=================================================================
Total params: 31,937,098
Trainable params: 31,936,458
Non-trainable params: 640
_________________________________________________________________
Epoch 1/3
188/188 [==============================] - 19s 81ms/step - loss: 0.8264 - accuracy: 0.7398 - val_loss: 3.4644 - val_accuracy: 0.1245
Epoch 2/3
188/188 [==============================] - 14s 75ms/step - loss: 0.4896 - accuracy: 0.8283 - val_loss: 1.2240 - val_accuracy: 0.5802
Epoch 3/3
188/188 [==============================] - 14s 77ms/step - loss: 0.4055 - accuracy: 0.8544 - val_loss: 0.3711 - val_accuracy: 0.8675
313/313 [==============================] - 2s 5ms/step - loss: 0.3850 - accuracy: 0.8591
[0.3849639296531677, 0.8590999841690063]
INFO:tensorflow:Assets written to: ram://****/assets
['model.pkl']
- Load your model
- Continue Training
Code for the above steps:
model = joblib.load("/content/model.pkl")
model.fit(X_train, y_train, batch_size=256, epochs=2, verbose=1, validation_split=.2)
model.evaluate(X_test, y_test, verbose=1)
Output:
Epoch 1/2
188/188 [==============================] - 17s 84ms/step - loss: 0.4414 - accuracy: 0.8496 - val_loss: 0.3449 - val_accuracy: 0.8697
Epoch 2/2
188/188 [==============================] - 15s 82ms/step - loss: 0.3704 - accuracy: 0.8708 - val_loss: 0.2884 - val_accuracy: 0.8965
313/313 [==============================] - 1s 5ms/step - loss: 0.3114 - accuracy: 0.8938
[0.31136029958724976, 0.8938000202178955]
CodePudding user response：
Below applies to any deep learning library …
- Build model
- Train model.
- Save model (should be saving parameters/weights as well).
- Load model from the saved file (any time any where).
- Continue with more training.