加载保存的Tensorflow模型后，我无法运行前向传播功能。我能够成功地提取权重，但是当我尝试将新输入传递给前向支持函数时，它会尝试使用未初始化的值＆＃39;错误。
我的占位符如下：
x = tf.placeholder('int64', [None, 4], name='input') # Number of examples x features
y = tf.placeholder('int64', [None, 1], name='output') # Number of examples x output
前向道具功能：
def forwardProp(x, y):
embedding_mat = tf.get_variable("EM", shape=[total_vocab, e_features], initializer=tf.random_normal_initializer(seed=1))
# m x words x total_vocab * total_vocab x e_features = m x words x e_features
# embed_x = tf.tensordot(x, tf.transpose(embedding_mat), axes=[[2], [0]])
# embed_y = tf.tensordot(y, tf.transpose(embedding_mat), axes=[[2], [0]])
embed_x = tf.gather(embedding_mat, x) # m x words x e_features
embed_y = tf.gather(embedding_mat, y) # m x words x e_features
#print("Shape of embed x", embed_x.get_shape())
W1 = tf.get_variable("W1", shape=[n1, e_features], initializer=tf.random_normal_initializer(seed=1))
B1 = tf.get_variable("b1", shape=[1, 4, n1], initializer=tf.zeros_initializer())
# m x words x e_features * e_features x n1 = m x words x n1
Z1 = tf.add(tf.tensordot(embed_x, tf.transpose(W1), axes=[[2], [0]]), B1, )
A1 = tf.nn.tanh(Z1)
W2 = tf.get_variable("W2", shape=[n2, n1], initializer=tf.random_normal_initializer(seed=1))
B2 = tf.get_variable("B2", shape=[1, 4, n2], initializer=tf.zeros_initializer())
# m x words x n1 * n1 x n2 = m x words x n2
Z2 = tf.add(tf.tensordot(A1, tf.transpose(W2), axes=[[2], [0]]), B2)
A2 = tf.nn.tanh(Z2)
W3 = tf.get_variable("W3", shape=[n3, n2], initializer=tf.random_normal_initializer(seed=1))
B3 = tf.get_variable("B3", shape=[1, 4, n3], initializer=tf.zeros_initializer())
# m x words x n2 * n2 x n3 = m x words x n3
Z3 = tf.add(tf.tensordot(A2, tf.transpose(W3), axes=[[2], [0]]), B3)
A3 = tf.nn.tanh(Z3)
# Convert m x words x n3 to m x n3
x_final = tf.reduce_mean(A3, axis=1)
y_final = tf.reduce_mean(embed_y, axis=1)
return x_final, y_final
支撑功能：
def backProp(X_index, Y_index):
x_final, y_final = forwardProp(x, y)
cost = tf.nn.l2_loss(x_final - y_final)
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)
init = tf.global_variables_initializer()
saver = tf.train.Saver()
total_batches = math.floor(m/batch_size)
with tf.Session() as sess:
sess.run(init)
for epoch in range(epochs):
batch_start = 0
for i in range(int(m/batch_size)):
x_hot = X_index[batch_start: batch_start + batch_size]
y_hot = Y_index[batch_start: batch_start + batch_size]
batch_start += batch_size
_, temp_cost = sess.run([optimizer, cost], feed_dict={x: x_hot, y: y_hot})
print("Cost at minibatch: ", i , " and epoch ", epoch, " is ", temp_cost)
if m % batch_size != 0:
x_hot = X_index[batch_start: batch_start+m - (batch_size*total_batches)]
y_hot = Y_index[batch_start: batch_start+m - (batch_size*total_batches)]
_, temp_cost = sess.run([optimizer, cost], feed_dict={x: x_hot, y: y_hot})
print("Cost at minibatch: (beyond floor) and epoch ", epoch, " is ", temp_cost)
# Saving the model
save_path = saver.save(sess, "./model_neural_embeddingV1.ckpt")
print("Model saved!")
通过调用预测函数重新加载模型：
def predict_search():
# Initialize variables
total_features = 4
extra = len(word_to_indice)
query = input('Enter your query')
words = word_tokenize(query)
# For now, it will throw an error if a word not present in dictionary is present
features = [word_to_indice[w.lower()] for w in words]
len_features = len(features)
X_query = []
Y_query = [[0]] # Dummy variable, we don't care about the Y query while doing prediction
if len_features < total_features:
features += [extra] * (total_features - len_features)
elif len_features > total_features:
features = features[:total_features]
X_query.append(features)
X_query = np.array(X_query)
print(X_query)
Y_query = np.array(Y_query)
# Load the model
init_global = tf.global_variables_initializer()
init_local = tf.local_variables_initializer()
#X_final, Y_final = forwardProp(x, y)
with tf.Session() as sess:
sess.run(init_global)
sess.run(init_local)
saver = tf.train.import_meta_graph('./model_neural_embeddingV1.ckpt.meta')
saver.restore(sess, './model_neural_embeddingV1.ckpt')
print("Model loaded")
print("Loaded variables are: ")
print(tf.trainable_variables())
print(sess.graph.get_operations())
embedMat = sess.run('EM:0') # Get the word embedding matrix
W1 = sess.run('W1:0')
b1 = sess.run('b1:0')
W2 = sess.run('W2:0')
b2 = sess.run('B2:0')
print(b2)
W3 = sess.run('W3:0')
b3 = sess.run('B3:0')
**#This part is not working, calling forward prop gives an 'attempting to use uninitialized value' error.**
X_final = sess.run(forwardProp(x, y), feed_dict={x: X_query, y: Y_query})
print(X_final)
答案 0 :(得分：1)
从元图中加载后，您无意中使用forwardProp函数创建了一堆图变量，有效地复制了变量而无意这样做。
您应该重构代码，以便在创建会话之前遵循创建图形变量的最佳做法。
例如，在名为build_graph的函数中创建所有变量。您可以在创建会话之前致电build_graph，但之后再也不会。这样可以避免这样的混淆。
您几乎应该总是避免从sess.run调用函数，例如您正在执行的操作：
X_final = sess.run(forwardProp(x, y), feed_dict={x: X_query, y: Y_query})
你一直在寻找错误。
注意forwardProp(x, y)正在创建张量流构造，所有权重和偏差的情况。
但请注意，您在这两行代码中创建了这些代码：
saver = tf.train.import_meta_graph('./model_neural_embeddingV1.ckpt.meta')
saver.restore(sess, './model_neural_embeddingV1.ckpt')
您可能尝试做的另一个选择是不使用import_meta_graph。您可以创建所有张量流OP和变量，然后运行saver.restore以恢复检查点，该检查点将检查点数据映射到您已创建的变量中。
请注意，您在tensorflow中实际上有两个选项，这有点令人困惑。您最终完成了两项工作（导入包含所有OP和变量的图表），以及重新创建图表。你必须选择一个。
我通常选择第一个选项，不要使用import_meta_graph，只需通过调用build_graph函数以编程方式重新创建图表。然后拨打saver.restore以启用检查点。当然，您将重新使用build_graph功能进行培训以及推理时间，这样您最终都会使用相同的图表。