Wsrtjtyk

The following is the code trying to use the input image as a training variable during the optimization. It starts with a keras model and convert that to a tensorflow model. This tensorflow model take a tensor as input and try to optimize the cost function using the input tensor as a trainable variable.

The error is:

NotImplementedError: ('Trying to update a Tensor ', )

The reason is that the input tensor is not a variable. The question is how to make the input image trainable or cast a tensor to a tf.variable. Help is appreciated:

import tensorflow as tf

from keras.models import Sequential, load_model, Model

from keras import backend as K

from keras.layers.core import Dense, Dropout, Activation

import os

from tensorflow.python.framework import graph_util

from tensorflow.python.framework import graph_io



n_classes = 10



model = Sequential()

model.add(Dense(10, input_shape=(784,)))

model.add(Activation('relu'))                            

model.add(Dense(n_classes, name='logits'))

model.add(Activation('softmax'))

model.compile(loss='categorical_crossentropy', metrics=['accuracy'],  optimizer='adam')



# Write the graph in binary .pb file

outdir = "model4_tf"

try:

    os.mkdir(outdir )

except:

    pass





prefix = "simple_nn" 

name = 'output_graph.pb'

# Alias the outputs in the model - this sometimes makes them easier to access in TF

pred = 

pred_node_names = 

for i, o in enumerate(model.outputs):

    pred_node_names.append(prefix+'_'+str(i))

    pred.append(tf.identity(o, 

                            name=pred_node_names[i]))

print('Output nodes names are: ', pred_node_names)





sess = K.get_session()





constant_graph = graph_util.convert_variables_to_constants(sess,                                         

sess.graph.as_graph_def(), pred_node_names)

graph_io.write_graph(constant_graph, outdir, name, as_text=False)





tf.reset_default_graph()



def load_graph(model_name):

    #graph = tf.Graph()

    graph = tf.get_default_graph()

    graph_def = tf.GraphDef()

    with open(model_name, "rb") as f:

        graph_def.ParseFromString(f.read())

    with graph.as_default():

        tf.import_graph_def(graph_def)

    return graph



my_graph = load_graph(model_name=os.path.join(outdir, name))





# In[15]:



input_op = my_graph.get_operation_by_name("import/dense_1_input")

output_op = my_graph.get_operation_by_name("import/simple_nn_0")

logit_op = my_graph.get_operation_by_name("import/logits/BiasAdd")





x_hat = input_op.outputs[0] # input tensor

labels = output_op.outputs[0] # label tensor

logits = logit_op.outputs[0] # logits tensor



learning_rate = tf.placeholder(tf.float32, ())



loss = tf.nn.softmax_cross_entropy_with_logits_v2(logits=logits, labels=[labels])

optim_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss,     var_list=[x_hat])