Keras: How to save model and continue training?

Question

I have a model that I've trained for 40 epochs. I kept checkpoints for each epochs, and I have also saved the model with model.save() . The code for training is:

n_units = 1000
model = Sequential()
model.add(LSTM(n_units, input_shape=(None, vec_size), return_sequences=True))
model.add(Dropout(0.2))
model.add(LSTM(n_units, return_sequences=True))
model.add(Dropout(0.2))
model.add(LSTM(n_units))
model.add(Dropout(0.2))
model.add(Dense(vec_size, activation='linear'))
model.compile(loss='mean_squared_error', optimizer='adam')
# define the checkpoint
filepath="word2vec-{epoch:02d}-{loss:.4f}.hdf5"
checkpoint = ModelCheckpoint(filepath, monitor='loss', verbose=1, save_best_only=True, mode='min')
callbacks_list = [checkpoint]
# fit the model
model.fit(x, y, epochs=40, batch_size=50, callbacks=callbacks_list)

However, when I load the model and try training it again, it starts all over as if it hasn't been trained before. The loss doesn't start from the last training.

What confuses me is when I load the model and redefine the model structure and use load_weight , model.predict() works well. Thus, I believe the model weights are loaded:

model = Sequential()
model.add(LSTM(n_units, input_shape=(None, vec_size), return_sequences=True))
model.add(Dropout(0.2))
model.add(LSTM(n_units, return_sequences=True))
model.add(Dropout(0.2))
model.add(LSTM(n_units))
model.add(Dropout(0.2))
model.add(Dense(vec_size, activation='linear'))
filename = "word2vec-39-0.0027.hdf5"
model.load_weights(filename)
model.compile(loss='mean_squared_error', optimizer='adam')

However, When I continue training with this, the loss is as high as the initial stage:

filepath="word2vec-{epoch:02d}-{loss:.4f}.hdf5"
checkpoint = ModelCheckpoint(filepath, monitor='loss', verbose=1, save_best_only=True, mode='min')
callbacks_list = [checkpoint]
# fit the model
model.fit(x, y, epochs=40, batch_size=50, callbacks=callbacks_list)

I searched and found some examples of saving and loading models here and here . However, none of them work.

---

Update 1

I looked at this question , tried it and it works:

model.save('partly_trained.h5')
del model
load_model('partly_trained.h5')

But when I close Python and reopen it, then run load_model again, it fails. The loss is as high as the initial state.

---

Update 2

I tried Yu-Yang's example code and it works. However, when I use my code again, it still failed.

This is result form the original training. The second epoch should start with loss = 3.1***:

13700/13846 [============================>.] - ETA: 0s - loss: 3.0519
13750/13846 [============================>.] - ETA: 0s - loss: 3.0511
13800/13846 [============================>.] - ETA: 0s - loss: 3.0512Epoch 00000: loss improved from inf to 3.05101, saving model to LPT-00-3.0510.h5

13846/13846 [==============================] - 81s - loss: 3.0510    
Epoch 2/60

   50/13846 [..............................] - ETA: 80s - loss: 3.1754
  100/13846 [..............................] - ETA: 78s - loss: 3.1174
  150/13846 [..............................] - ETA: 78s - loss: 3.0745

I closed Python, reopened it, loaded the model with model = load_model("LPT-00-3.0510.h5") then train with:

filepath="LPT-{epoch:02d}-{loss:.4f}.h5"
checkpoint = ModelCheckpoint(filepath, monitor='loss', verbose=1, save_best_only=True, mode='min')
callbacks_list = [checkpoint]
# fit the model
model.fit(x, y, epochs=60, batch_size=50, callbacks=callbacks_list)

The loss starts with 4.54:

Epoch 1/60
   50/13846 [..............................] - ETA: 162s - loss: 4.5451
   100/13846 [..............................] - ETA: 113s - loss: 4.3835

Answer 1

I have a model that I've trained for 40 epochs. I kept checkpoints for each epochs, and I have also saved the model with model.save() . The code for training is:

n_units = 1000
model = Sequential()
model.add(LSTM(n_units, input_shape=(None, vec_size), return_sequences=True))
model.add(Dropout(0.2))
model.add(LSTM(n_units, return_sequences=True))
model.add(Dropout(0.2))
model.add(LSTM(n_units))
model.add(Dropout(0.2))
model.add(Dense(vec_size, activation='linear'))
model.compile(loss='mean_squared_error', optimizer='adam')
# define the checkpoint
filepath="word2vec-{epoch:02d}-{loss:.4f}.hdf5"
checkpoint = ModelCheckpoint(filepath, monitor='loss', verbose=1, save_best_only=True, mode='min')
callbacks_list = [checkpoint]
# fit the model
model.fit(x, y, epochs=40, batch_size=50, callbacks=callbacks_list)

However, when I load the model and try training it again, it starts all over as if it hasn't been trained before. The loss doesn't start from the last training.

What confuses me is when I load the model and redefine the model structure and use load_weight , model.predict() works well. Thus, I believe the model weights are loaded:

model = Sequential()
model.add(LSTM(n_units, input_shape=(None, vec_size), return_sequences=True))
model.add(Dropout(0.2))
model.add(LSTM(n_units, return_sequences=True))
model.add(Dropout(0.2))
model.add(LSTM(n_units))
model.add(Dropout(0.2))
model.add(Dense(vec_size, activation='linear'))
filename = "word2vec-39-0.0027.hdf5"
model.load_weights(filename)
model.compile(loss='mean_squared_error', optimizer='adam')

However, When I continue training with this, the loss is as high as the initial stage:

filepath="word2vec-{epoch:02d}-{loss:.4f}.hdf5"
checkpoint = ModelCheckpoint(filepath, monitor='loss', verbose=1, save_best_only=True, mode='min')
callbacks_list = [checkpoint]
# fit the model
model.fit(x, y, epochs=40, batch_size=50, callbacks=callbacks_list)

I searched and found some examples of saving and loading models here and here . However, none of them work.

---

Update 1

I looked at this question , tried it and it works:

model.save('partly_trained.h5')
del model
load_model('partly_trained.h5')

But when I close Python and reopen it, then run load_model again, it fails. The loss is as high as the initial state.

---

Update 2

I tried Yu-Yang's example code and it works. However, when I use my code again, it still failed.

This is result form the original training. The second epoch should start with loss = 3.1***:

13700/13846 [============================>.] - ETA: 0s - loss: 3.0519
13750/13846 [============================>.] - ETA: 0s - loss: 3.0511
13800/13846 [============================>.] - ETA: 0s - loss: 3.0512Epoch 00000: loss improved from inf to 3.05101, saving model to LPT-00-3.0510.h5

13846/13846 [==============================] - 81s - loss: 3.0510    
Epoch 2/60

   50/13846 [..............................] - ETA: 80s - loss: 3.1754
  100/13846 [..............................] - ETA: 78s - loss: 3.1174
  150/13846 [..............................] - ETA: 78s - loss: 3.0745

I closed Python, reopened it, loaded the model with model = load_model("LPT-00-3.0510.h5") then train with:

filepath="LPT-{epoch:02d}-{loss:.4f}.h5"
checkpoint = ModelCheckpoint(filepath, monitor='loss', verbose=1, save_best_only=True, mode='min')
callbacks_list = [checkpoint]
# fit the model
model.fit(x, y, epochs=60, batch_size=50, callbacks=callbacks_list)

The loss starts with 4.54:

Epoch 1/60
   50/13846 [..............................] - ETA: 162s - loss: 4.5451
   100/13846 [..............................] - ETA: 113s - loss: 4.3835

Answer 2

I have a model that I've trained for 40 epochs. I kept checkpoints for each epochs, and I have also saved the model with model.save() . The code for training is:

n_units = 1000
model = Sequential()
model.add(LSTM(n_units, input_shape=(None, vec_size), return_sequences=True))
model.add(Dropout(0.2))
model.add(LSTM(n_units, return_sequences=True))
model.add(Dropout(0.2))
model.add(LSTM(n_units))
model.add(Dropout(0.2))
model.add(Dense(vec_size, activation='linear'))
model.compile(loss='mean_squared_error', optimizer='adam')
# define the checkpoint
filepath="word2vec-{epoch:02d}-{loss:.4f}.hdf5"
checkpoint = ModelCheckpoint(filepath, monitor='loss', verbose=1, save_best_only=True, mode='min')
callbacks_list = [checkpoint]
# fit the model
model.fit(x, y, epochs=40, batch_size=50, callbacks=callbacks_list)

However, when I load the model and try training it again, it starts all over as if it hasn't been trained before. The loss doesn't start from the last training.

What confuses me is when I load the model and redefine the model structure and use load_weight , model.predict() works well. Thus, I believe the model weights are loaded:

model = Sequential()
model.add(LSTM(n_units, input_shape=(None, vec_size), return_sequences=True))
model.add(Dropout(0.2))
model.add(LSTM(n_units, return_sequences=True))
model.add(Dropout(0.2))
model.add(LSTM(n_units))
model.add(Dropout(0.2))
model.add(Dense(vec_size, activation='linear'))
filename = "word2vec-39-0.0027.hdf5"
model.load_weights(filename)
model.compile(loss='mean_squared_error', optimizer='adam')

However, When I continue training with this, the loss is as high as the initial stage:

filepath="word2vec-{epoch:02d}-{loss:.4f}.hdf5"
checkpoint = ModelCheckpoint(filepath, monitor='loss', verbose=1, save_best_only=True, mode='min')
callbacks_list = [checkpoint]
# fit the model
model.fit(x, y, epochs=40, batch_size=50, callbacks=callbacks_list)

I searched and found some examples of saving and loading models here and here . However, none of them work.

---

Update 1

I looked at this question , tried it and it works:

model.save('partly_trained.h5')
del model
load_model('partly_trained.h5')

But when I close Python and reopen it, then run load_model again, it fails. The loss is as high as the initial state.

---

Update 2

I tried Yu-Yang's example code and it works. However, when I use my code again, it still failed.

This is result form the original training. The second epoch should start with loss = 3.1***:

13700/13846 [============================>.] - ETA: 0s - loss: 3.0519
13750/13846 [============================>.] - ETA: 0s - loss: 3.0511
13800/13846 [============================>.] - ETA: 0s - loss: 3.0512Epoch 00000: loss improved from inf to 3.05101, saving model to LPT-00-3.0510.h5

13846/13846 [==============================] - 81s - loss: 3.0510    
Epoch 2/60

   50/13846 [..............................] - ETA: 80s - loss: 3.1754
  100/13846 [..............................] - ETA: 78s - loss: 3.1174
  150/13846 [..............................] - ETA: 78s - loss: 3.0745

I closed Python, reopened it, loaded the model with model = load_model("LPT-00-3.0510.h5") then train with:

filepath="LPT-{epoch:02d}-{loss:.4f}.h5"
checkpoint = ModelCheckpoint(filepath, monitor='loss', verbose=1, save_best_only=True, mode='min')
callbacks_list = [checkpoint]
# fit the model
model.fit(x, y, epochs=60, batch_size=50, callbacks=callbacks_list)

The loss starts with 4.54:

Epoch 1/60
   50/13846 [..............................] - ETA: 162s - loss: 4.5451
   100/13846 [..............................] - ETA: 113s - loss: 4.3835

Answer 3

I have a model that I've trained for 40 epochs. I kept checkpoints for each epochs, and I have also saved the model with model.save() . The code for training is:

n_units = 1000
model = Sequential()
model.add(LSTM(n_units, input_shape=(None, vec_size), return_sequences=True))
model.add(Dropout(0.2))
model.add(LSTM(n_units, return_sequences=True))
model.add(Dropout(0.2))
model.add(LSTM(n_units))
model.add(Dropout(0.2))
model.add(Dense(vec_size, activation='linear'))
model.compile(loss='mean_squared_error', optimizer='adam')
# define the checkpoint
filepath="word2vec-{epoch:02d}-{loss:.4f}.hdf5"
checkpoint = ModelCheckpoint(filepath, monitor='loss', verbose=1, save_best_only=True, mode='min')
callbacks_list = [checkpoint]
# fit the model
model.fit(x, y, epochs=40, batch_size=50, callbacks=callbacks_list)

However, when I load the model and try training it again, it starts all over as if it hasn't been trained before. The loss doesn't start from the last training.

What confuses me is when I load the model and redefine the model structure and use load_weight , model.predict() works well. Thus, I believe the model weights are loaded:

model = Sequential()
model.add(LSTM(n_units, input_shape=(None, vec_size), return_sequences=True))
model.add(Dropout(0.2))
model.add(LSTM(n_units, return_sequences=True))
model.add(Dropout(0.2))
model.add(LSTM(n_units))
model.add(Dropout(0.2))
model.add(Dense(vec_size, activation='linear'))
filename = "word2vec-39-0.0027.hdf5"
model.load_weights(filename)
model.compile(loss='mean_squared_error', optimizer='adam')

However, When I continue training with this, the loss is as high as the initial stage:

filepath="word2vec-{epoch:02d}-{loss:.4f}.hdf5"
checkpoint = ModelCheckpoint(filepath, monitor='loss', verbose=1, save_best_only=True, mode='min')
callbacks_list = [checkpoint]
# fit the model
model.fit(x, y, epochs=40, batch_size=50, callbacks=callbacks_list)

I searched and found some examples of saving and loading models here and here . However, none of them work.

---

Update 1

I looked at this question , tried it and it works:

model.save('partly_trained.h5')
del model
load_model('partly_trained.h5')

But when I close Python and reopen it, then run load_model again, it fails. The loss is as high as the initial state.

---

Update 2

I tried Yu-Yang's example code and it works. However, when I use my code again, it still failed.

This is result form the original training. The second epoch should start with loss = 3.1***:

13700/13846 [============================>.] - ETA: 0s - loss: 3.0519
13750/13846 [============================>.] - ETA: 0s - loss: 3.0511
13800/13846 [============================>.] - ETA: 0s - loss: 3.0512Epoch 00000: loss improved from inf to 3.05101, saving model to LPT-00-3.0510.h5

13846/13846 [==============================] - 81s - loss: 3.0510    
Epoch 2/60

   50/13846 [..............................] - ETA: 80s - loss: 3.1754
  100/13846 [..............................] - ETA: 78s - loss: 3.1174
  150/13846 [..............................] - ETA: 78s - loss: 3.0745

I closed Python, reopened it, loaded the model with model = load_model("LPT-00-3.0510.h5") then train with:

filepath="LPT-{epoch:02d}-{loss:.4f}.h5"
checkpoint = ModelCheckpoint(filepath, monitor='loss', verbose=1, save_best_only=True, mode='min')
callbacks_list = [checkpoint]
# fit the model
model.fit(x, y, epochs=60, batch_size=50, callbacks=callbacks_list)

The loss starts with 4.54:

Epoch 1/60
   50/13846 [..............................] - ETA: 162s - loss: 4.5451
   100/13846 [..............................] - ETA: 113s - loss: 4.3835

Answer 4

I have a model that I've trained for 40 epochs. I kept checkpoints for each epochs, and I have also saved the model with model.save() . The code for training is:

n_units = 1000
model = Sequential()
model.add(LSTM(n_units, input_shape=(None, vec_size), return_sequences=True))
model.add(Dropout(0.2))
model.add(LSTM(n_units, return_sequences=True))
model.add(Dropout(0.2))
model.add(LSTM(n_units))
model.add(Dropout(0.2))
model.add(Dense(vec_size, activation='linear'))
model.compile(loss='mean_squared_error', optimizer='adam')
# define the checkpoint
filepath="word2vec-{epoch:02d}-{loss:.4f}.hdf5"
checkpoint = ModelCheckpoint(filepath, monitor='loss', verbose=1, save_best_only=True, mode='min')
callbacks_list = [checkpoint]
# fit the model
model.fit(x, y, epochs=40, batch_size=50, callbacks=callbacks_list)

However, when I load the model and try training it again, it starts all over as if it hasn't been trained before. The loss doesn't start from the last training.

What confuses me is when I load the model and redefine the model structure and use load_weight , model.predict() works well. Thus, I believe the model weights are loaded:

model = Sequential()
model.add(LSTM(n_units, input_shape=(None, vec_size), return_sequences=True))
model.add(Dropout(0.2))
model.add(LSTM(n_units, return_sequences=True))
model.add(Dropout(0.2))
model.add(LSTM(n_units))
model.add(Dropout(0.2))
model.add(Dense(vec_size, activation='linear'))
filename = "word2vec-39-0.0027.hdf5"
model.load_weights(filename)
model.compile(loss='mean_squared_error', optimizer='adam')

However, When I continue training with this, the loss is as high as the initial stage:

filepath="word2vec-{epoch:02d}-{loss:.4f}.hdf5"
checkpoint = ModelCheckpoint(filepath, monitor='loss', verbose=1, save_best_only=True, mode='min')
callbacks_list = [checkpoint]
# fit the model
model.fit(x, y, epochs=40, batch_size=50, callbacks=callbacks_list)

I searched and found some examples of saving and loading models here and here . However, none of them work.

---

Update 1

I looked at this question , tried it and it works:

model.save('partly_trained.h5')
del model
load_model('partly_trained.h5')

But when I close Python and reopen it, then run load_model again, it fails. The loss is as high as the initial state.

---

Update 2

I tried Yu-Yang's example code and it works. However, when I use my code again, it still failed.

This is result form the original training. The second epoch should start with loss = 3.1***:

13700/13846 [============================>.] - ETA: 0s - loss: 3.0519
13750/13846 [============================>.] - ETA: 0s - loss: 3.0511
13800/13846 [============================>.] - ETA: 0s - loss: 3.0512Epoch 00000: loss improved from inf to 3.05101, saving model to LPT-00-3.0510.h5

13846/13846 [==============================] - 81s - loss: 3.0510    
Epoch 2/60

   50/13846 [..............................] - ETA: 80s - loss: 3.1754
  100/13846 [..............................] - ETA: 78s - loss: 3.1174
  150/13846 [..............................] - ETA: 78s - loss: 3.0745

I closed Python, reopened it, loaded the model with model = load_model("LPT-00-3.0510.h5") then train with:

filepath="LPT-{epoch:02d}-{loss:.4f}.h5"
checkpoint = ModelCheckpoint(filepath, monitor='loss', verbose=1, save_best_only=True, mode='min')
callbacks_list = [checkpoint]
# fit the model
model.fit(x, y, epochs=60, batch_size=50, callbacks=callbacks_list)

The loss starts with 4.54:

Epoch 1/60
   50/13846 [..............................] - ETA: 162s - loss: 4.5451
   100/13846 [..............................] - ETA: 113s - loss: 4.3835

Answer 5

I have a model that I've trained for 40 epochs. I kept checkpoints for each epochs, and I have also saved the model with model.save() . The code for training is:

n_units = 1000
model = Sequential()
model.add(LSTM(n_units, input_shape=(None, vec_size), return_sequences=True))
model.add(Dropout(0.2))
model.add(LSTM(n_units, return_sequences=True))
model.add(Dropout(0.2))
model.add(LSTM(n_units))
model.add(Dropout(0.2))
model.add(Dense(vec_size, activation='linear'))
model.compile(loss='mean_squared_error', optimizer='adam')
# define the checkpoint
filepath="word2vec-{epoch:02d}-{loss:.4f}.hdf5"
checkpoint = ModelCheckpoint(filepath, monitor='loss', verbose=1, save_best_only=True, mode='min')
callbacks_list = [checkpoint]
# fit the model
model.fit(x, y, epochs=40, batch_size=50, callbacks=callbacks_list)

However, when I load the model and try training it again, it starts all over as if it hasn't been trained before. The loss doesn't start from the last training.

What confuses me is when I load the model and redefine the model structure and use load_weight , model.predict() works well. Thus, I believe the model weights are loaded:

model = Sequential()
model.add(LSTM(n_units, input_shape=(None, vec_size), return_sequences=True))
model.add(Dropout(0.2))
model.add(LSTM(n_units, return_sequences=True))
model.add(Dropout(0.2))
model.add(LSTM(n_units))
model.add(Dropout(0.2))
model.add(Dense(vec_size, activation='linear'))
filename = "word2vec-39-0.0027.hdf5"
model.load_weights(filename)
model.compile(loss='mean_squared_error', optimizer='adam')

However, When I continue training with this, the loss is as high as the initial stage:

filepath="word2vec-{epoch:02d}-{loss:.4f}.hdf5"
checkpoint = ModelCheckpoint(filepath, monitor='loss', verbose=1, save_best_only=True, mode='min')
callbacks_list = [checkpoint]
# fit the model
model.fit(x, y, epochs=40, batch_size=50, callbacks=callbacks_list)

I searched and found some examples of saving and loading models here and here . However, none of them work.

---

Update 1

I looked at this question , tried it and it works:

model.save('partly_trained.h5')
del model
load_model('partly_trained.h5')

But when I close Python and reopen it, then run load_model again, it fails. The loss is as high as the initial state.

---

Update 2

I tried Yu-Yang's example code and it works. However, when I use my code again, it still failed.

This is result form the original training. The second epoch should start with loss = 3.1***:

13700/13846 [============================>.] - ETA: 0s - loss: 3.0519
13750/13846 [============================>.] - ETA: 0s - loss: 3.0511
13800/13846 [============================>.] - ETA: 0s - loss: 3.0512Epoch 00000: loss improved from inf to 3.05101, saving model to LPT-00-3.0510.h5

13846/13846 [==============================] - 81s - loss: 3.0510    
Epoch 2/60

   50/13846 [..............................] - ETA: 80s - loss: 3.1754
  100/13846 [..............................] - ETA: 78s - loss: 3.1174
  150/13846 [..............................] - ETA: 78s - loss: 3.0745

I closed Python, reopened it, loaded the model with model = load_model("LPT-00-3.0510.h5") then train with:

filepath="LPT-{epoch:02d}-{loss:.4f}.h5"
checkpoint = ModelCheckpoint(filepath, monitor='loss', verbose=1, save_best_only=True, mode='min')
callbacks_list = [checkpoint]
# fit the model
model.fit(x, y, epochs=60, batch_size=50, callbacks=callbacks_list)

The loss starts with 4.54:

Epoch 1/60
   50/13846 [..............................] - ETA: 162s - loss: 4.5451
   100/13846 [..............................] - ETA: 113s - loss: 4.3835

Answer 6

I have a model that I've trained for 40 epochs. I kept checkpoints for each epochs, and I have also saved the model with model.save() . The code for training is:

n_units = 1000
model = Sequential()
model.add(LSTM(n_units, input_shape=(None, vec_size), return_sequences=True))
model.add(Dropout(0.2))
model.add(LSTM(n_units, return_sequences=True))
model.add(Dropout(0.2))
model.add(LSTM(n_units))
model.add(Dropout(0.2))
model.add(Dense(vec_size, activation='linear'))
model.compile(loss='mean_squared_error', optimizer='adam')
# define the checkpoint
filepath="word2vec-{epoch:02d}-{loss:.4f}.hdf5"
checkpoint = ModelCheckpoint(filepath, monitor='loss', verbose=1, save_best_only=True, mode='min')
callbacks_list = [checkpoint]
# fit the model
model.fit(x, y, epochs=40, batch_size=50, callbacks=callbacks_list)

However, when I load the model and try training it again, it starts all over as if it hasn't been trained before. The loss doesn't start from the last training.

What confuses me is when I load the model and redefine the model structure and use load_weight , model.predict() works well. Thus, I believe the model weights are loaded:

model = Sequential()
model.add(LSTM(n_units, input_shape=(None, vec_size), return_sequences=True))
model.add(Dropout(0.2))
model.add(LSTM(n_units, return_sequences=True))
model.add(Dropout(0.2))
model.add(LSTM(n_units))
model.add(Dropout(0.2))
model.add(Dense(vec_size, activation='linear'))
filename = "word2vec-39-0.0027.hdf5"
model.load_weights(filename)
model.compile(loss='mean_squared_error', optimizer='adam')

However, When I continue training with this, the loss is as high as the initial stage:

filepath="word2vec-{epoch:02d}-{loss:.4f}.hdf5"
checkpoint = ModelCheckpoint(filepath, monitor='loss', verbose=1, save_best_only=True, mode='min')
callbacks_list = [checkpoint]
# fit the model
model.fit(x, y, epochs=40, batch_size=50, callbacks=callbacks_list)

I searched and found some examples of saving and loading models here and here . However, none of them work.

---

Update 1

I looked at this question , tried it and it works:

model.save('partly_trained.h5')
del model
load_model('partly_trained.h5')

But when I close Python and reopen it, then run load_model again, it fails. The loss is as high as the initial state.

---

Update 2

I tried Yu-Yang's example code and it works. However, when I use my code again, it still failed.

This is result form the original training. The second epoch should start with loss = 3.1***:

13700/13846 [============================>.] - ETA: 0s - loss: 3.0519
13750/13846 [============================>.] - ETA: 0s - loss: 3.0511
13800/13846 [============================>.] - ETA: 0s - loss: 3.0512Epoch 00000: loss improved from inf to 3.05101, saving model to LPT-00-3.0510.h5

13846/13846 [==============================] - 81s - loss: 3.0510    
Epoch 2/60

   50/13846 [..............................] - ETA: 80s - loss: 3.1754
  100/13846 [..............................] - ETA: 78s - loss: 3.1174
  150/13846 [..............................] - ETA: 78s - loss: 3.0745

I closed Python, reopened it, loaded the model with model = load_model("LPT-00-3.0510.h5") then train with:

filepath="LPT-{epoch:02d}-{loss:.4f}.h5"
checkpoint = ModelCheckpoint(filepath, monitor='loss', verbose=1, save_best_only=True, mode='min')
callbacks_list = [checkpoint]
# fit the model
model.fit(x, y, epochs=60, batch_size=50, callbacks=callbacks_list)

The loss starts with 4.54:

Epoch 1/60
   50/13846 [..............................] - ETA: 162s - loss: 4.5451
   100/13846 [..............................] - ETA: 113s - loss: 4.3835

Answer 7

I have a model that I've trained for 40 epochs. I kept checkpoints for each epochs, and I have also saved the model with model.save() . The code for training is:

n_units = 1000
model = Sequential()
model.add(LSTM(n_units, input_shape=(None, vec_size), return_sequences=True))
model.add(Dropout(0.2))
model.add(LSTM(n_units, return_sequences=True))
model.add(Dropout(0.2))
model.add(LSTM(n_units))
model.add(Dropout(0.2))
model.add(Dense(vec_size, activation='linear'))
model.compile(loss='mean_squared_error', optimizer='adam')
# define the checkpoint
filepath="word2vec-{epoch:02d}-{loss:.4f}.hdf5"
checkpoint = ModelCheckpoint(filepath, monitor='loss', verbose=1, save_best_only=True, mode='min')
callbacks_list = [checkpoint]
# fit the model
model.fit(x, y, epochs=40, batch_size=50, callbacks=callbacks_list)

However, when I load the model and try training it again, it starts all over as if it hasn't been trained before. The loss doesn't start from the last training.

What confuses me is when I load the model and redefine the model structure and use load_weight , model.predict() works well. Thus, I believe the model weights are loaded:

model = Sequential()
model.add(LSTM(n_units, input_shape=(None, vec_size), return_sequences=True))
model.add(Dropout(0.2))
model.add(LSTM(n_units, return_sequences=True))
model.add(Dropout(0.2))
model.add(LSTM(n_units))
model.add(Dropout(0.2))
model.add(Dense(vec_size, activation='linear'))
filename = "word2vec-39-0.0027.hdf5"
model.load_weights(filename)
model.compile(loss='mean_squared_error', optimizer='adam')

However, When I continue training with this, the loss is as high as the initial stage:

filepath="word2vec-{epoch:02d}-{loss:.4f}.hdf5"
checkpoint = ModelCheckpoint(filepath, monitor='loss', verbose=1, save_best_only=True, mode='min')
callbacks_list = [checkpoint]
# fit the model
model.fit(x, y, epochs=40, batch_size=50, callbacks=callbacks_list)

I searched and found some examples of saving and loading models here and here . However, none of them work.

---

Update 1

I looked at this question , tried it and it works:

model.save('partly_trained.h5')
del model
load_model('partly_trained.h5')

But when I close Python and reopen it, then run load_model again, it fails. The loss is as high as the initial state.

---

Update 2

I tried Yu-Yang's example code and it works. However, when I use my code again, it still failed.

This is result form the original training. The second epoch should start with loss = 3.1***:

13700/13846 [============================>.] - ETA: 0s - loss: 3.0519
13750/13846 [============================>.] - ETA: 0s - loss: 3.0511
13800/13846 [============================>.] - ETA: 0s - loss: 3.0512Epoch 00000: loss improved from inf to 3.05101, saving model to LPT-00-3.0510.h5

13846/13846 [==============================] - 81s - loss: 3.0510    
Epoch 2/60

   50/13846 [..............................] - ETA: 80s - loss: 3.1754
  100/13846 [..............................] - ETA: 78s - loss: 3.1174
  150/13846 [..............................] - ETA: 78s - loss: 3.0745

I closed Python, reopened it, loaded the model with model = load_model("LPT-00-3.0510.h5") then train with:

filepath="LPT-{epoch:02d}-{loss:.4f}.h5"
checkpoint = ModelCheckpoint(filepath, monitor='loss', verbose=1, save_best_only=True, mode='min')
callbacks_list = [checkpoint]
# fit the model
model.fit(x, y, epochs=60, batch_size=50, callbacks=callbacks_list)

The loss starts with 4.54:

Epoch 1/60
   50/13846 [..............................] - ETA: 162s - loss: 4.5451
   100/13846 [..............................] - ETA: 113s - loss: 4.3835

Answer 8

Since Keras and Tensorflow are now bundled, you can use the newer Tensorflow format that will save all model info including the optimizer and its state (from the doc , emphasis mine):

You can save an entire model to a single artifact. It will include:

• The model's architecture/config
• The model's weight values (which were learned during training)
• The model's compilation information (if compile() was called)
• The optimizer and its state, if any (this enables you to restart training where you left)

APIs

• model.save() or tf.keras.models.save_model()
• tf.keras.models.load_model()

So once your model is saved that way, you can load it and resume training: it will continue where it left off.