Wie kann ich mehrere Modelle in einem Python-Modul in TFLearn trainieren?

Question

Ich versuche, zwei Modelle in einem Python-Modul mit TFLearn zu trainieren. Ich verwende restore=False für alle Schichten. Ich erhalte Fehler, wenn die Anpassungsmethode des zweiten Modells genannt wird:

Traceback (most recent call last): 
    File "multiple_models.py", line 76, in <module> 
    a_model.fit(X_inputs=X, Y_targets=Y, validation_set=0.1, show_metric=True, batch_size=None, shuffle=True, n_epoch=20) # 100% of data being used for validation 
    File "/Users/swarbhanu/miniconda2/lib/python2.7/site-packages/tflearn/models/dnn.py", line 182, in fit 
    self.targets) 
    File "/Users/swarbhanu/miniconda2/lib/python2.7/site-packages/tflearn/utils.py", line 289, in feed_dict_builder 
    feed_dict[net_inputs[i]] = x 
IndexError: list index out of range 

Dieser Fehler geschieht nicht, wenn eines der Modelle auf Kommentar gesetzt ist und daher nur ein Modell trainiert wird. Jede Hilfe wäre großartig! Ich habe (soweit ich das beurteilen kann) alle vorherigen Stack-Überlauf-Fragen über Probleme mit dem Training oder dem Laden mehrerer Modelle in tflearn oder tensorflow durchgesehen, aber die vorgeschlagenen Lösungen (zB restore=False oder mit variables_scope) funktionierten nicht für mich. In meinem Nutzungsszenario ist es sehr wichtig, ein Modul zu verwenden, um mehrere Modelle zu trainieren (und später zu laden und anzupassen). Der Code ist unten:

import os.path 
import numpy as np 
import tflearn 
from tflearn.layers.core import input_data, fully_connected 
from tflearn.layers.normalization import batch_normalization 
from tflearn.layers.recurrent import bidirectional_rnn, BasicLSTMCell 
from tflearn.layers.estimator import regression 

import tensorflow as tf 

i_model_file = 'example1.tfl' 
a_model_file = 'example2.tfl' 

batch_size = 50 
sequence_len = 10 
sequence_unit_array_size = 300 
output_array_size = 1 

# Set parameters 
i_num_lstm_units = 128 
i_num_labels = 5 
i_learning_rate = 0.001 

a_num_lstm_units = 128 
a_num_labels = 4 
a_learning_rate = 0.001 

def create_data(batch_size, sequence_len, sequence_unit_array_size, num_labels): 
    shape_x = (batch_size,sequence_len,sequence_unit_array_size) 
    shape_y = (batch_size, num_labels) 
    X = np.random.random(shape_x) 
    Y = np.zeros(shape_y) 
    ind = np.random.randint(low=0,high=num_labels,size=batch_size) 
    for b in xrange(batch_size): 
     Y[b][ind[b]] = 1 
    return X, Y 

def create_classification_model(target_name, num_lstm_units, num_labels, learning_rate, saved_model_file): 
    with tf.variable_scope(target_name): 
     input_layer = input_data(shape=[None, sequence_len, sequence_unit_array_size]) 
     conv = tflearn.conv_1d(input_layer, nb_filter=2, filter_size=3, regularizer='L2', weight_decay=0.0001,restore=False) 
     bnorm1 = batch_normalization(conv,restore=False) 
     birnn = bidirectional_rnn(bnorm1, BasicLSTMCell(num_lstm_units), BasicLSTMCell(num_lstm_units)) 
     bnorm2 = batch_normalization(birnn, restore=False) 
     conn = fully_connected(bnorm2, n_units=num_labels, activation='softmax',restore=False) 
     regress = regression(conn, optimizer='adam', learning_rate= learning_rate, loss='categorical_crossentropy', shuffle_batches=True,restore=False) 
     model = tflearn.DNN(regress, clip_gradients=0., tensorboard_verbose=3) 

     return model 

i_model = create_classification_model('intent', num_lstm_units=i_num_lstm_units, num_labels=i_num_labels, learning_rate=i_learning_rate, saved_model_file=i_model_file) 

# Get data 
X, Y = create_data(batch_size = batch_size, sequence_len = sequence_len, sequence_unit_array_size = sequence_unit_array_size, num_labels=i_num_labels) 

for overalliter in xrange(1): 
    i_model.fit(X_inputs=X, Y_targets=Y, validation_set=0.1, show_metric=True, batch_size=None, shuffle=True, 
       n_epoch=20) # 100% of data being used for validation 
    i_model.save(i_model_file) 

    # Predicting on sample sentences 
    X_new, _ = create_data(batch_size = 1, sequence_len = sequence_len, sequence_unit_array_size = sequence_unit_array_size, num_labels=i_num_labels) 
    Y_new = i_model.predict(X_new) 

    print "X_new: ", X_new 
    print "Y_predicted: ", Y_new 

a_model = create_classification_model('action', num_lstm_units=a_num_lstm_units, num_labels=a_num_labels, learning_rate=a_learning_rate, saved_model_file=a_model_file) 
print a_model 

# Training data 
X, Y = create_data(batch_size = batch_size, sequence_len = sequence_len, sequence_unit_array_size = sequence_unit_array_size, num_labels=a_num_labels) 

for overalliter in xrange(1): 
    a_model.fit(X_inputs=X, Y_targets=Y, validation_set=0.1, show_metric=True, batch_size=None, shuffle=True, n_epoch=20) # 100% of data being used for validation 
    a_model.save(a_model_file) 

    # Predicting on sample sentences 
    X_new, _ = create_data(batch_size = 1, sequence_len = sequence_len, sequence_unit_array_size = sequence_unit_array_size, num_labels=a_num_labels) 
    Y_new = a_model.predict(X_new) 

    print "X_new: ", X_new 
    print "Y_predicted: ", Y_new 

Answer 1

Ich hatte das gleiche Problem.

with tf.Graph().as_default(): vor i_model = create_classification_model and a_model = create_classification_model setzen und richtig einrücken.

Oder überprüfen, wie es hier https://github.com/tflearn/tflearn/blob/master/examples/basics/logical.py

getan