Gefrorene Modell von Keras nicht vorhersagen, nach der Restaurierung

Question

ich Keras bin mit meinem Modell zu bauen und zu trainieren. Modell sieht wie folgt aus:

inputs = Input(shape=(input_size, 3), dtype='float32', name='input') 
lstm1 = LSTM(128, return_sequences=True)(inputs) 
dropout1 = Dropout(0.5)(lstm1) 
lstm2 = LSTM(128)(dropout1) 
dropout2 = Dropout(0.5)(lstm2) 
outputs = Dense(output_size, activation='softmax', name='output')(dropout2) 

Kurz vor einem Kontrollpunkt machen meine Modellklassen vorhersagen kann sehr gut (Klassen Verteilung nach softmax):

[[ 0.00117011 0.00631532 0.10080294 0.84386677 0.04784485]] 

jedoch übernächsten Code:

all_saver = tf.train.Saver() 
sess.run(tf.global_variables_initializer()) 
print save_path + '/model_predeploy.chkp' 
all_saver.save(sess, save_path + '/model_predeploy.chkp', meta_graph_suffix='meta', write_meta_graph=True) 
tf.train.write_graph(sess.graph_def, save_path, "model.pb", False) 

und das Einfrieren sie mit

bazel-bin/tensorflow/python/tools/freeze_graph --input_graph=/Users/denisermolin/Work/Projects/MotionRecognitionTraining/model/graph/model.pb --input_checkpoint=/Users/denisermolin/Work/Projects/MotionRecognitionTraining/model/graph/model_predeploy.chkp --output_graph=/Users/denisermolin/Work/Projects/MotionRecognitionTraining/model/graph/output.pb --output_node_names=Softmax --input_binary=true 

Und es nach

graph = load_graph(args.frozen_model_filename) 

    # We can verify that we can access the list of operations in the graph 
    for op in graph.get_operations(): 
     print(op.name) 

    # We access the input and output nodes 
    x = graph.get_tensor_by_name('input:0') 
    y = graph.get_tensor_by_name('Softmax:0') 

    data = [7.4768066E-4,-0.02217102,0.07727051,7.4768066E-4,-0.02217102,0.07727051,7.4768066E-4,-0.02217102,0.07727051,0.004989624,-0.020874023,0.09140015,0.004989624,-0.020874023,0.09140015,0.010604858,-0.010665894,0.025527954,0.010299683,0.018035889,-0.052749634,-0.012786865,0.017837524,-0.020828247,-0.045898438,0.007095337,0.01550293,-0.06680298,0.013702393,0.02687073,-0.061767578,0.026550291,-1.373291E-4,-0.036621094,0.041778564,-0.011276245,-0.042678833,0.054336548,0.036697388,-0.07182312,0.036483765,0.081726074,-0.08639526,0.041793823,0.07392883,-0.051788326,0.07649231,0.092178345,-0.056396484,0.0771637,0.11044311,-0.08444214,0.06201172,0.0920105,-0.12609863,0.06137085,0.104537964,-0.14356995,0.079071045,0.11187744,-0.17756653,0.08576965,0.16818237,-0.2379303,0.07879639,0.19819641,-0.2631073,0.13290405,0.19137573,-0.23666382,0.21955872,0.16033936,-0.23666382,0.21955872,0.16033936,-0.22547913,0.23838806,0.27246094,-0.26376343,0.19580078,0.33566284,-0.26376343,0.19580078,0.33566284,-0.4733429,0.19911194,-0.0050811768,-0.48905945,0.14544678,-0.21205139,-0.48905945,0.14544678,-0.21205139,-0.37893677,0.15655518,-0.1382904,-0.27426147,0.16381836,-0.052841187,-0.21949767,0.18780518,-0.045913696,-0.28207397,0.17993164,-0.1550293,-0.37120056,0.13322449,-0.4617462,-0.3773346,0.17321777,-0.7678375,-0.20349121,0.12588501,-0.7908478,-4.8828125E-4,0.116516106,-0.57121277,-0.090042114,0.08895874,-0.3849945,-0.232193,-0.028884886,-0.4724579,-0.19163513,-0.06340027,-0.5598297,-0.068481445,-0.025268555,-0.54397583,-0.03288269,-0.12750244,-0.48367307,0.0057525635,-0.030532837,-0.45234683,0.099868774,-0.0070648193,-0.57225037,0.21514893,0.05860901,-0.5052185,0.3602295,0.14176941,-0.4087372,0.57940674,0.16700745,-0.35438538,0.75743103,0.2631073,-0.5294647,0.75743103,0.2631073,-0.5294647,0.74624634,0.2193451,-0.70674133,0.91960144,0.29077148,-0.7026367,0.91960144,0.29077148,-0.7026367,0.81611633,0.34953308,-0.50927734,0.8429718,0.41278076,-0.38298035,0.84576416,0.4597778,-0.15159607,0.9177856,0.47735596,0.099731445,0.9820862,0.57232666,0.20970154,0.9269562,0.5357971,0.45666504,0.7898865,0.48097226,0.5698242,0.5332794,0.4213867,0.6626892,0.5032654,0.4464111,0.59614563,0.5827484,0.4588318,0.8383636,0.60975647,0.46882626,1.050766,0.58917236,0.52201843,0.9510345,0.48217773,0.502121,0.8063202,0.24050903,0.42752075,0.81951904,0.10655212,0.43006897,0.7798157,0.15496826,0.5040283,0.7533417,0.18733215,0.55770874,0.63716125,0.22062683,0.5880585,0.503067,0.06762695,0.49337766,0.6584778,-0.14086914,0.4414215,0.615036,-0.14086914,0.4414215,0.615036,-0.03614807,0.6751251,0.06636047,-0.03614807,0.6751251,0.06636047,0.17774963,0.741272,-0.09466553,0.21842958,0.7971039,-0.050811768,0.06843567,0.7729645,-0.34933472,-0.2092285,0.5443878,-0.5428009,-0.43028256,0.37249756,-0.5168762,-0.23457338,0.3491211,-0.45985416,0.15863037,0.49960327,-0.5370636,0.31782532,0.5680084,-0.8007355,0.1651001,0.5300598,-0.87919617,-0.086135864,0.49140927,-0.6066437,-0.20877077,0.4261017,-0.55911255,-0.33840942,0.34194946,-0.7007904,-0.36250305,0.27163696,-0.76208496,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0] 
    data = np.reshape(data, [1, 142, 3]) 

    # We launch a Session 
    with tf.Session(graph=graph) as sess: 
     # Note: we didn't initialize/restore anything, everything is stored in the graph_def 
     y_out = sess.run(y, feed_dict={ 
      x: data 
     }) 
     print(y_out) 

Gibt mir ein gleichmäßige Verteilung über alle Bedrucken von Etikett:

[[ 0.20328824 0.19835895 0.19692752 0.20159255 0.19983278]] 

ich etwas falsch tue? Mit tensorflow 0.12, weil ich nicht 1.0 laufen auf Android machen. Dies ist eine andere Geschichte. Alles wurde gebaut, trainiert und exportiert mit 0.12.

Answer 1

all_saver = tf.train.Saver() 
sess.run(tf.global_variables_initializer()) 
print save_path + '/model_predeploy.chkp' 
all_saver.save(sess, save_path + '/model_predeploy.chkp', meta_graph_suffix='meta', write_meta_graph=True) 
tf.train.write_graph(sess.graph_def, save_path, "model.pb", False) 

In Zeile 2, Sie neu initialisieren alle Variablen von Grund auf neu (nicht nur die nicht initialisiert sind). Dies bedeutet, dass Ihr Modell trainierte an diesem Punkt weg ist, und Sie sparen ein Modell, das nur zufällig/konstante Gewichte ist (abhängig von Ihrer initializers).

Demo-Skript:

from __future__ import absolute_import 
from __future__ import division 
from __future__ import print_function 

import tensorflow as tf 
import numpy as np 

var = tf.get_variable('demo', dtype=tf.float32, shape=[], 
         initializer=tf.zeros_initializer()) 

sess = tf.Session() 

sess.run(tf.assign(var, 42)); 

print(var.eval(session=sess)) 

Dieser druckt 42.

sess.run(tf.global_variables_initializer()) 

print(var.eval(session=sess)) 

Dieser druckt 0, als die Variable also auf 0

neu initialisiert wurde, Ihre Variablen initialisieren, bevor Trainieren Sie Ihr Modell und initialisieren Sie es nicht bevor Sie es schreiben.