There are three ways of model training: using pre-defined fit method, using pre-defined tran_on_batch method, using customized training loop.
Note: fit_generator method is not recommended in tf.keras since it has been merged into fit.
import numpy as np
import pandas as pd
import tensorflow as tf
from tensorflow.keras import *
# Time stamps
@tf.function
def printbar():
ts = tf.timestamp()
today_ts = ts%(24*60*60)
hour = tf.cast(today_ts//3600+8,tf.int32)%tf.constant(24)
minite = tf.cast((today_ts%3600)//60,tf.int32)
second = tf.cast(tf.floor(today_ts%60),tf.int32)
def timeformat(m):
if tf.strings.length(tf.strings.format("{}",m))==1:
return(tf.strings.format("0{}",m))
else:
return(tf.strings.format("{}",m))
timestring = tf.strings.join([timeformat(hour),timeformat(minite),
timeformat(second)],separator = ":")
tf.print("=========="*8,end = "")
tf.print(timestring)
MAX_LEN = 300
BATCH_SIZE = 32
(x_train,y_train),(x_test,y_test) = datasets.reuters.load_data()
x_train = preprocessing.sequence.pad_sequences(x_train,maxlen=MAX_LEN)
x_test = preprocessing.sequence.pad_sequences(x_test,maxlen=MAX_LEN)
MAX_WORDS = x_train.max()+1
CAT_NUM = y_train.max()+1
ds_train = tf.data.Dataset.from_tensor_slices((x_train,y_train)) \
.shuffle(buffer_size = 1000).batch(BATCH_SIZE) \
.prefetch(tf.data.experimental.AUTOTUNE).cache()
ds_test = tf.data.Dataset.from_tensor_slices((x_test,y_test)) \
.shuffle(buffer_size = 1000).batch(BATCH_SIZE) \
.prefetch(tf.data.experimental.AUTOTUNE).cache()This is a powerful method, which supports training the data with types of numpy array, tf.data.Dataset and Python generator.
This method also supports complicated logical controlling through proper configuration of the callbacks.
tf.keras.backend.clear_session()
def create_model():
model = models.Sequential()
model.add(layers.Embedding(MAX_WORDS,7,input_length=MAX_LEN))
model.add(layers.Conv1D(filters = 64,kernel_size = 5,activation = "relu"))
model.add(layers.MaxPool1D(2))
model.add(layers.Conv1D(filters = 32,kernel_size = 3,activation = "relu"))
model.add(layers.MaxPool1D(2))
model.add(layers.Flatten())
model.add(layers.Dense(CAT_NUM,activation = "softmax"))
return(model)
def compile_model(model):
model.compile(optimizer=optimizers.Nadam(),
loss=losses.SparseCategoricalCrossentropy(),
metrics=[metrics.SparseCategoricalAccuracy(),metrics.SparseTopKCategoricalAccuracy(5)])
return(model)
model = create_model()
model.summary()
model = compile_model(model)Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
embedding (Embedding) (None, 300, 7) 216874
_________________________________________________________________
conv1d (Conv1D) (None, 296, 64) 2304
_________________________________________________________________
max_pooling1d (MaxPooling1D) (None, 148, 64) 0
_________________________________________________________________
conv1d_1 (Conv1D) (None, 146, 32) 6176
_________________________________________________________________
max_pooling1d_1 (MaxPooling1 (None, 73, 32) 0
_________________________________________________________________
flatten (Flatten) (None, 2336) 0
_________________________________________________________________
dense (Dense) (None, 46) 107502
=================================================================
Total params: 332,856
Trainable params: 332,856
Non-trainable params: 0
_________________________________________________________________
history = model.fit(ds_train,validation_data = ds_test,epochs = 10)Train for 281 steps, validate for 71 steps
Epoch 1/10
281/281 [==============================] - 11s 37ms/step - loss: 2.0231 - sparse_categorical_accuracy: 0.4636 - sparse_top_k_categorical_accuracy: 0.7450 - val_loss: 1.7346 - val_sparse_categorical_accuracy: 0.5534 - val_sparse_top_k_categorical_accuracy: 0.7560
Epoch 2/10
281/281 [==============================] - 9s 31ms/step - loss: 1.5079 - sparse_categorical_accuracy: 0.6091 - sparse_top_k_categorical_accuracy: 0.7901 - val_loss: 1.5475 - val_sparse_categorical_accuracy: 0.6109 - val_sparse_top_k_categorical_accuracy: 0.7792
Epoch 3/10
281/281 [==============================] - 9s 33ms/step - loss: 1.2204 - sparse_categorical_accuracy: 0.6823 - sparse_top_k_categorical_accuracy: 0.8448 - val_loss: 1.5455 - val_sparse_categorical_accuracy: 0.6367 - val_sparse_top_k_categorical_accuracy: 0.8001
Epoch 4/10
281/281 [==============================] - 9s 33ms/step - loss: 0.9382 - sparse_categorical_accuracy: 0.7543 - sparse_top_k_categorical_accuracy: 0.9075 - val_loss: 1.6780 - val_sparse_categorical_accuracy: 0.6398 - val_sparse_top_k_categorical_accuracy: 0.8032
Epoch 5/10
281/281 [==============================] - 10s 34ms/step - loss: 0.6791 - sparse_categorical_accuracy: 0.8255 - sparse_top_k_categorical_accuracy: 0.9513 - val_loss: 1.9426 - val_sparse_categorical_accuracy: 0.6376 - val_sparse_top_k_categorical_accuracy: 0.7956
Epoch 6/10
281/281 [==============================] - 9s 33ms/step - loss: 0.5063 - sparse_categorical_accuracy: 0.8762 - sparse_top_k_categorical_accuracy: 0.9716 - val_loss: 2.2141 - val_sparse_categorical_accuracy: 0.6291 - val_sparse_top_k_categorical_accuracy: 0.7947
Epoch 7/10
281/281 [==============================] - 10s 37ms/step - loss: 0.4031 - sparse_categorical_accuracy: 0.9050 - sparse_top_k_categorical_accuracy: 0.9817 - val_loss: 2.4126 - val_sparse_categorical_accuracy: 0.6264 - val_sparse_top_k_categorical_accuracy: 0.7947
Epoch 8/10
281/281 [==============================] - 10s 35ms/step - loss: 0.3380 - sparse_categorical_accuracy: 0.9205 - sparse_top_k_categorical_accuracy: 0.9881 - val_loss: 2.5366 - val_sparse_categorical_accuracy: 0.6242 - val_sparse_top_k_categorical_accuracy: 0.7974
Epoch 9/10
281/281 [==============================] - 10s 36ms/step - loss: 0.2921 - sparse_categorical_accuracy: 0.9299 - sparse_top_k_categorical_accuracy: 0.9909 - val_loss: 2.6564 - val_sparse_categorical_accuracy: 0.6242 - val_sparse_top_k_categorical_accuracy: 0.7983
Epoch 10/10
281/281 [==============================] - 9s 30ms/step - loss: 0.2613 - sparse_categorical_accuracy: 0.9334 - sparse_top_k_categorical_accuracy: 0.9947 - val_loss: 2.7365 - val_sparse_categorical_accuracy: 0.6220 - val_sparse_top_k_categorical_accuracy: 0.8005
This pre-defined method allows fine-controlling to the training procedure for each batch without the callbacks, which is even more flexible than fit method.
tf.keras.backend.clear_session()
def create_model():
model = models.Sequential()
model.add(layers.Embedding(MAX_WORDS,7,input_length=MAX_LEN))
model.add(layers.Conv1D(filters = 64,kernel_size = 5,activation = "relu"))
model.add(layers.MaxPool1D(2))
model.add(layers.Conv1D(filters = 32,kernel_size = 3,activation = "relu"))
model.add(layers.MaxPool1D(2))
model.add(layers.Flatten())
model.add(layers.Dense(CAT_NUM,activation = "softmax"))
return(model)
def compile_model(model):
model.compile(optimizer=optimizers.Nadam(),
loss=losses.SparseCategoricalCrossentropy(),
metrics=[metrics.SparseCategoricalAccuracy(),metrics.SparseTopKCategoricalAccuracy(5)])
return(model)
model = create_model()
model.summary()
model = compile_model(model)Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
embedding (Embedding) (None, 300, 7) 216874
_________________________________________________________________
conv1d (Conv1D) (None, 296, 64) 2304
_________________________________________________________________
max_pooling1d (MaxPooling1D) (None, 148, 64) 0
_________________________________________________________________
conv1d_1 (Conv1D) (None, 146, 32) 6176
_________________________________________________________________
max_pooling1d_1 (MaxPooling1 (None, 73, 32) 0
_________________________________________________________________
flatten (Flatten) (None, 2336) 0
_________________________________________________________________
dense (Dense) (None, 46) 107502
=================================================================
Total params: 332,856
Trainable params: 332,856
Non-trainable params: 0
_________________________________________________________________
def train_model(model,ds_train,ds_valid,epoches):
for epoch in tf.range(1,epoches+1):
model.reset_metrics()
# Reduce learning rate at the late stage of training.
if epoch == 5:
model.optimizer.lr.assign(model.optimizer.lr/2.0)
tf.print("Lowering optimizer Learning Rate...\n\n")
for x, y in ds_train:
train_result = model.train_on_batch(x, y)
for x, y in ds_valid:
valid_result = model.test_on_batch(x, y,reset_metrics=False)
if epoch%1 ==0:
printbar()
tf.print("epoch = ",epoch)
print("train:",dict(zip(model.metrics_names,train_result)))
print("valid:",dict(zip(model.metrics_names,valid_result)))
print("")train_model(model,ds_train,ds_test,10)================================================================================13:09:19
epoch = 1
train: {'loss': 0.82411176, 'sparse_categorical_accuracy': 0.77272725, 'sparse_top_k_categorical_accuracy': 0.8636364}
valid: {'loss': 1.9265995, 'sparse_categorical_accuracy': 0.5743544, 'sparse_top_k_categorical_accuracy': 0.75779164}
================================================================================13:09:27
epoch = 2
train: {'loss': 0.6006621, 'sparse_categorical_accuracy': 0.90909094, 'sparse_top_k_categorical_accuracy': 0.95454544}
valid: {'loss': 1.844159, 'sparse_categorical_accuracy': 0.6126447, 'sparse_top_k_categorical_accuracy': 0.7920748}
================================================================================13:09:35
epoch = 3
train: {'loss': 0.36935613, 'sparse_categorical_accuracy': 0.90909094, 'sparse_top_k_categorical_accuracy': 0.95454544}
valid: {'loss': 2.163433, 'sparse_categorical_accuracy': 0.63312554, 'sparse_top_k_categorical_accuracy': 0.8045414}
================================================================================13:09:42
epoch = 4
train: {'loss': 0.2304088, 'sparse_categorical_accuracy': 0.90909094, 'sparse_top_k_categorical_accuracy': 1.0}
valid: {'loss': 2.8911984, 'sparse_categorical_accuracy': 0.6344613, 'sparse_top_k_categorical_accuracy': 0.7978629}
Lowering optimizer Learning Rate...
================================================================================13:09:51
epoch = 5
train: {'loss': 0.111194365, 'sparse_categorical_accuracy': 0.95454544, 'sparse_top_k_categorical_accuracy': 1.0}
valid: {'loss': 3.6431572, 'sparse_categorical_accuracy': 0.6295637, 'sparse_top_k_categorical_accuracy': 0.7978629}
================================================================================13:09:59
epoch = 6
train: {'loss': 0.07741702, 'sparse_categorical_accuracy': 0.95454544, 'sparse_top_k_categorical_accuracy': 1.0}
valid: {'loss': 4.074161, 'sparse_categorical_accuracy': 0.6255565, 'sparse_top_k_categorical_accuracy': 0.794301}
================================================================================13:10:07
epoch = 7
train: {'loss': 0.056113098, 'sparse_categorical_accuracy': 1.0, 'sparse_top_k_categorical_accuracy': 1.0}
valid: {'loss': 4.4461513, 'sparse_categorical_accuracy': 0.6273375, 'sparse_top_k_categorical_accuracy': 0.79652715}
================================================================================13:10:17
epoch = 8
train: {'loss': 0.043448802, 'sparse_categorical_accuracy': 1.0, 'sparse_top_k_categorical_accuracy': 1.0}
valid: {'loss': 4.7687583, 'sparse_categorical_accuracy': 0.6224399, 'sparse_top_k_categorical_accuracy': 0.79741764}
================================================================================13:10:26
epoch = 9
train: {'loss': 0.035002146, 'sparse_categorical_accuracy': 1.0, 'sparse_top_k_categorical_accuracy': 1.0}
valid: {'loss': 5.130505, 'sparse_categorical_accuracy': 0.6175423, 'sparse_top_k_categorical_accuracy': 0.794301}
================================================================================13:10:34
epoch = 10
train: {'loss': 0.028303564, 'sparse_categorical_accuracy': 1.0, 'sparse_top_k_categorical_accuracy': 1.0}
valid: {'loss': 5.4559293, 'sparse_categorical_accuracy': 0.6148709, 'sparse_top_k_categorical_accuracy': 0.7947462}
Re-compilation of the model is not required in the customized training loop, just back-propagate the iterative parameters through the optimizer according to the loss function, which gives us the highest flexibility.
tf.keras.backend.clear_session()
def create_model():
model = models.Sequential()
model.add(layers.Embedding(MAX_WORDS,7,input_length=MAX_LEN))
model.add(layers.Conv1D(filters = 64,kernel_size = 5,activation = "relu"))
model.add(layers.MaxPool1D(2))
model.add(layers.Conv1D(filters = 32,kernel_size = 3,activation = "relu"))
model.add(layers.MaxPool1D(2))
model.add(layers.Flatten())
model.add(layers.Dense(CAT_NUM,activation = "softmax"))
return(model)
model = create_model()
model.summary()optimizer = optimizers.Nadam()
loss_func = losses.SparseCategoricalCrossentropy()
train_loss = metrics.Mean(name='train_loss')
train_metric = metrics.SparseCategoricalAccuracy(name='train_accuracy')
valid_loss = metrics.Mean(name='valid_loss')
valid_metric = metrics.SparseCategoricalAccuracy(name='valid_accuracy')
@tf.function
def train_step(model, features, labels):
with tf.GradientTape() as tape:
predictions = model(features,training = True)
loss = loss_func(labels, predictions)
gradients = tape.gradient(loss, model.trainable_variables)
optimizer.apply_gradients(zip(gradients, model.trainable_variables))
train_loss.update_state(loss)
train_metric.update_state(labels, predictions)
@tf.function
def valid_step(model, features, labels):
predictions = model(features)
batch_loss = loss_func(labels, predictions)
valid_loss.update_state(batch_loss)
valid_metric.update_state(labels, predictions)
def train_model(model,ds_train,ds_valid,epochs):
for epoch in tf.range(1,epochs+1):
for features, labels in ds_train:
train_step(model,features,labels)
for features, labels in ds_valid:
valid_step(model,features,labels)
logs = 'Epoch={},Loss:{},Accuracy:{},Valid Loss:{},Valid Accuracy:{}'
if epoch%1 ==0:
printbar()
tf.print(tf.strings.format(logs,
(epoch,train_loss.result(),train_metric.result(),valid_loss.result(),valid_metric.result())))
tf.print("")
train_loss.reset_states()
valid_loss.reset_states()
train_metric.reset_states()
valid_metric.reset_states()
train_model(model,ds_train,ds_test,10)================================================================================13:12:03
Epoch=1,Loss:2.02051544,Accuracy:0.460253835,Valid Loss:1.75700927,Valid Accuracy:0.536954582
================================================================================13:12:09
Epoch=2,Loss:1.510795,Accuracy:0.610665798,Valid Loss:1.55349839,Valid Accuracy:0.616206586
================================================================================13:12:17
Epoch=3,Loss:1.19221532,Accuracy:0.696170092,Valid Loss:1.52315605,Valid Accuracy:0.651380241
================================================================================13:12:23
Epoch=4,Loss:0.90101546,Accuracy:0.766310394,Valid Loss:1.68327653,Valid Accuracy:0.648263574
================================================================================13:12:30
Epoch=5,Loss:0.655430496,Accuracy:0.831329346,Valid Loss:1.90872383,Valid Accuracy:0.641139805
================================================================================13:12:37
Epoch=6,Loss:0.492730737,Accuracy:0.877866864,Valid Loss:2.09966016,Valid Accuracy:0.63223511
================================================================================13:12:44
Epoch=7,Loss:0.391238362,Accuracy:0.904030263,Valid Loss:2.27431226,Valid Accuracy:0.625111282
================================================================================13:12:51
Epoch=8,Loss:0.327761739,Accuracy:0.922066331,Valid Loss:2.42568827,Valid Accuracy:0.617542326
================================================================================13:12:58
Epoch=9,Loss:0.285573095,Accuracy:0.930527747,Valid Loss:2.55942106,Valid Accuracy:0.612644672
================================================================================13:13:05
Epoch=10,Loss:0.255482465,Accuracy:0.936094403,Valid Loss:2.67789412,Valid Accuracy:0.612199485
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