tflearn 保存模型重新训练

from：https://stackoverflow.com/questions/41616292/how-to-load-and-retrain-tflean-model

This is to create a graph and save it

graph1 = tf.Graph()
with graph1.as_default():
    network = input_data(shape=[None, MAX_DOCUMENT_LENGTH])
    network = tflearn.embedding(network, input_dim=n_words, output_dim=128)
    branch1 = conv_1d(network, 128, 3, padding='valid', activation='relu', regularizer="L2")
    branch2 = conv_1d(network, 128, 4, padding='valid', activation='relu', regularizer="L2")
    branch3 = conv_1d(network, 128, 5, padding='valid', activation='relu', regularizer="L2")
    network = merge([branch1, branch2, branch3], mode='concat', axis=1)
    network = tf.expand_dims(network, 2)
    network = global_max_pool(network)
    network = dropout(network, 0.5)
    network = fully_connected(network, 2, activation='softmax')
    network = regression(network, optimizer='adam', learning_rate=0.001,loss='categorical_crossentropy', name='target')
    model = tflearn.DNN(network, tensorboard_verbose=0)
    clf, acc, roc_auc,fpr,tpr =classify_DNN(data,clas,model)
    clf.save(model_path)

To reload and retrain or use it for prediction

MODEL = None
with tf.Graph().as_default():
## Building deep neural network
    network = input_data(shape=[None, MAX_DOCUMENT_LENGTH])
    network = tflearn.embedding(network, input_dim=n_words, output_dim=128)
    branch1 = conv_1d(network, 128, 3, padding='valid', activation='relu', regularizer="L2")
    branch2 = conv_1d(network, 128, 4, padding='valid', activation='relu', regularizer="L2")
    branch3 = conv_1d(network, 128, 5, padding='valid', activation='relu', regularizer="L2")
    network = merge([branch1, branch2, branch3], mode='concat', axis=1)
    network = tf.expand_dims(network, 2)
    network = global_max_pool(network)
    network = dropout(network, 0.5)
    network = fully_connected(network, 2, activation='softmax')
    network = regression(network, optimizer='adam', learning_rate=0.001,loss='categorical_crossentropy', name='target')
    new_model = tflearn.DNN(network, tensorboard_verbose=3)
    new_model.load(model_path)
    MODEL = new_model

Use the MODEL for prediction or retraining. The 1st line and the with loop was important. For anyone who might need help

官方例子：

""" An example showing how to save/restore models and retrieve weights. """

from __future__ import absolute_import, division, print_function

import tflearn

import tflearn.datasets.mnist as mnist

# MNIST Data
X, Y, testX, testY = mnist.load_data(one_hot=True)

# Model
input_layer = tflearn.input_data(shape=[None, 784], name='input')
dense1 = tflearn.fully_connected(input_layer, 128, name='dense1')
dense2 = tflearn.fully_connected(dense1, 256, name='dense2')
softmax = tflearn.fully_connected(dense2, 10, activation='softmax')
regression = tflearn.regression(softmax, optimizer='adam',
                                learning_rate=0.001,
                                loss='categorical_crossentropy')

# Define classifier, with model checkpoint (autosave)
model = tflearn.DNN(regression, checkpoint_path='model.tfl.ckpt')

# Train model, with model checkpoint every epoch and every 200 training steps.
model.fit(X, Y, n_epoch=1,
          validation_set=(testX, testY),
          show_metric=True,
          snapshot_epoch=True, # Snapshot (save & evaluate) model every epoch.
          snapshot_step=500, # Snapshot (save & evalaute) model every 500 steps.
          run_id='model_and_weights')

# ---------------------
# Save and load a model
# ---------------------

# Manually save model
model.save("model.tfl")

# Load a model
model.load("model.tfl")

# Or Load a model from auto-generated checkpoint
# >> model.load("model.tfl.ckpt-500")

# Resume training
model.fit(X, Y, n_epoch=1,
          validation_set=(testX, testY),
          show_metric=True,
          snapshot_epoch=True,
          run_id='model_and_weights')

# ------------------
# Retrieving weights
# ------------------

# Retrieve a layer weights, by layer name:
dense1_vars = tflearn.variables.get_layer_variables_by_name('dense1')
# Get a variable's value, using model `get_weights` method:
print("Dense1 layer weights:")
print(model.get_weights(dense1_vars[0]))
# Or using generic tflearn function:
print("Dense1 layer biases:")
with model.session.as_default():
    print(tflearn.variables.get_value(dense1_vars[1]))

# It is also possible to retrieve a layer weights through its attributes `W`
# and `b` (if available).
# Get variable's value, using model `get_weights` method:
print("Dense2 layer weights:")
print(model.get_weights(dense2.W))
# Or using generic tflearn function:
print("Dense2 layer biases:")
with model.session.as_default():
    print(tflearn.variables.get_value(dense2.b))