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Faire des prédictions à l'aide d'un graphique tensorflow à partir d'un modèle de kéros

J'ai un modèle formé à l'utilisation de Keras avec Tensorflow comme backend, mais maintenant je dois transformer mon modèle en un graphique tensorflow pour une certaine application. J'ai essayé de le faire et de faire des prédictions pour m'assurer que cela fonctionne correctement, mais en comparant les résultats collectés à partir de model.predict (), j'obtiens des valeurs très différentes. Par exemple:

from keras.models import load_model
import tensorflow as tf

model = load_model('model_file.h5')

x_placeholder = tf.placeholder(tf.float32, shape=(None,7214,1))
y = model(x_placeholder)

x = np.ones((1,7214,1))


with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    print("Predictions from:\ntf graph:      "+str(sess.run(y, feed_dict={x_placeholder:x})))
    print("keras predict: "+str(model.predict(x)))

retour:

Predictions from:
tf graph:      [[-0.1015993   0.07432419  0.0592984 ]]
keras predict: [[ 0.39339241  0.57949686 -3.67846966]]

Les valeurs des keras prédites sont correctes, mais les résultats du graphique tf ne le sont pas.

Si cela aide à connaître l'application finale prévue, je crée une matrice jacobienne avec la fonction tf.gradients (), mais actuellement, elle ne renvoie pas les résultats corrects lors de la comparaison avec la fonction jacobienne de theeano, qui donne le jacobien correct. Voici mon code jacobian tensorflow:

x = tf.placeholder(tf.float32, shape=(None,7214,1))
y = tf.reshape(model(x)[0],[-1])
y_list = tf.unstack(y)

jacobian_list = [tf.gradients(y_, x)[0] for y_ in y_list]
jacobian = tf.stack(jacobian_list)

EDIT: Code de modèle

import numpy as np

from keras.models import Sequential
from keras.layers import Dense, InputLayer, Flatten
from keras.layers.convolutional import Conv1D
from keras.layers.convolutional import MaxPooling1D
from keras.optimizers import Adam
from keras.callbacks import EarlyStopping, ReduceLROnPlateau

# activation function used following every layer except for the output layers
activation = 'relu'

# model weight initializer
initializer = 'he_normal'

# shape of input data that is fed into the input layer
input_shape = (None,7214,1)

# number of filters used in the convolutional layers
num_filters = [4,16]

# length of the filters in the convolutional layers
filter_length = 8

# length of the maxpooling window 
pool_length = 4

# number of nodes in each of the hidden fully connected layers
num_hidden_nodes = [256,128]

# number of samples fed into model at once during training
batch_size = 64

# maximum number of interations for model training
max_epochs = 30

# initial learning rate for optimization algorithm
lr = 0.0007

# exponential decay rate for the 1st moment estimates for optimization algorithm
beta_1 = 0.9

# exponential decay rate for the 2nd moment estimates for optimization algorithm
beta_2 = 0.999

# a small constant for numerical stability for optimization algorithm
optimizer_epsilon = 1e-08

model = Sequential([

    InputLayer(batch_input_shape=input_shape),

    Conv1D(kernel_initializer=initializer, activation=activation, padding="same", filters=num_filters[0], kernel_size=filter_length),

    Conv1D(kernel_initializer=initializer, activation=activation, padding="same", filters=num_filters[1], kernel_size=filter_length),

    MaxPooling1D(pool_size=pool_length),

    Flatten(),

    Dense(units=num_hidden_nodes[0], kernel_initializer=initializer, activation=activation),

    Dense(units=num_hidden_nodes[1], kernel_initializer=initializer, activation=activation),

    Dense(units=3, activation="linear", input_dim=num_hidden_nodes[1]),
]) 

# compile model
loss_function = mean squared error
early_stopping_min_delta = 0.0001
early_stopping_patience = 4
reduce_lr_factor = 0.5
reuce_lr_epsilon = 0.0009
reduce_lr_patience = 2
reduce_lr_min = 0.00008

optimizer = Adam(lr=lr, beta_1=beta_1, beta_2=beta_2, epsilon=optimizer_epsilon, decay=0.0)

early_stopping = EarlyStopping(monitor='val_loss',     min_delta=early_stopping_min_delta, 
                                   patience=early_stopping_patience, verbose=2, mode='min')

reduce_lr = ReduceLROnPlateau(monitor='loss', factor=0.5, epsilon=reuce_lr_epsilon, 
                              patience=reduce_lr_patience,     min_lr=reduce_lr_min, mode='min', verbose=2)

model.compile(optimizer=optimizer, loss=loss_function)

model.fit(train_x, train_y, validation_data=(cv_x, cv_y),
      epochs=max_epochs, batch_size=batch_size, verbose=2,
      callbacks=[reduce_lr,early_stopping])

model.save('model_file.h5')
pythonmachine-learningtensorflowkeras
25
Starnetter

@frankyjuang m'a lié ici

https://github.com/amir-abdi/keras_to_tensorflow

et en combinant cela avec le code de

https://github.com/metaflow-ai/blog/blob/master/tf-freeze/load.py

et

https://github.com/tensorflow/tensorflow/issues/675

J'ai trouvé une solution à la fois pour prédire à l'aide d'un graphique tf et pour créer la fonction jacobienne:

import tensorflow as tf
import numpy as np

# Create function to convert saved keras model to tensorflow graph
def convert_to_pb(weight_file,input_fld='',output_fld=''):

    import os
    import os.path as osp
    from tensorflow.python.framework import graph_util
    from tensorflow.python.framework import graph_io
    from keras.models import load_model
    from keras import backend as K


    # weight_file is a .h5 keras model file
    output_node_names_of_input_network = ["pred0"] 
    output_node_names_of_final_network = 'output_node'

    # change filename to a .pb tensorflow file
    output_graph_name = weight_file[:-2]+'pb'
    weight_file_path = osp.join(input_fld, weight_file)

    net_model = load_model(weight_file_path)

    num_output = len(output_node_names_of_input_network)
    pred = [None]*num_output
    pred_node_names = [None]*num_output

    for i in range(num_output):
        pred_node_names[i] = output_node_names_of_final_network+str(i)
        pred[i] = tf.identity(net_model.output[i], name=pred_node_names[i])

    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, output_fld, output_graph_name, as_text=False)
    print('saved the constant graph (ready for inference) at: ', osp.join(output_fld, output_graph_name))

    return output_fld+output_graph_name

Appel:

tf_model_path = convert_to_pb('model_file.h5','/model_dir/','/model_dir/')

Créer une fonction pour charger le modèle tf sous forme de graphique:

def load_graph(frozen_graph_filename):
    # We load the protobuf file from the disk and parse it to retrieve the 
    # unserialized graph_def
    with tf.gfile.GFile(frozen_graph_filename, "rb") as f:
        graph_def = tf.GraphDef()
        graph_def.ParseFromString(f.read())

    # Then, we can use again a convenient built-in function to import a graph_def into the 
    # current default Graph
    with tf.Graph().as_default() as graph:
        tf.import_graph_def(
            graph_def, 
            input_map=None, 
            return_elements=None, 
            name="prefix", 
            op_dict=None, 
            producer_op_list=None
        )

    input_name = graph.get_operations()[0].name+':0'
    output_name = graph.get_operations()[-1].name+':0'

    return graph, input_name, output_name

Créer une fonction pour faire des prédictions de modèle en utilisant le graphique tf

def predict(model_path, input_data):
    # load tf graph
    tf_model,tf_input,tf_output = load_graph(model_path)

    # Create tensors for model input and output
    x = tf_model.get_tensor_by_name(tf_input)
    y = tf_model.get_tensor_by_name(tf_output) 

    # Number of model outputs
    num_outputs = y.shape.as_list()[0]
    predictions = np.zeros((input_data.shape[0],num_outputs))
    for i in range(input_data.shape[0]):        
        with tf.Session(graph=tf_model) as sess:
            y_out = sess.run(y, feed_dict={x: input_data[i:i+1]})
            predictions[i] = y_out

    return predictions

Faire des prédictions:

tf_predictions = predict(tf_model_path,test_data)

Fonction jacobienne:

def compute_jacobian(model_path,input_data):

    tf_model,tf_input,tf_output = load_graph(model_path)    

    x = tf_model.get_tensor_by_name(tf_input)
    y = tf_model.get_tensor_by_name(tf_output)
    y_list = tf.unstack(y)
    num_outputs = y.shape.as_list()[0]
    jacobian = np.zeros((num_outputs,input_data.shape[0],input_data.shape[1]))
    for i in range(input_data.shape[0]):
        with tf.Session(graph=tf_model) as sess:
            y_out = sess.run([tf.gradients(y_, x)[0] for y_ in y_list], feed_dict={x: input_data[i:i+1]})
            jac_temp = np.asarray(y_out)
        jacobian[:,i:i+1,:]=jac_temp[:,:,:,0]
    return jacobian

Calculer la matrice jacobienne:

jacobians = compute_jacobian(tf_model_path,test_data)
15
Starnetter