【发布时间】：2018-06-05 10:12:31
【问题描述】：
情境化
我正在使用 tensorfow 构建图像识别模型。事实上，我正在尝试保存我的模型然后恢复它以进行预测。
程序
我用这种结构构建了一个 CNN
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_3 (Conv2D) (None, 204, 204, 32) 896
_________________________________________________________________
dropout_3 (Dropout) (None, 204, 204, 32) 0
_________________________________________________________________
conv2d_4 (Conv2D) (None, 204, 204, 32) 9248
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 102, 102, 32) 0
_________________________________________________________________
flatten_2 (Flatten) (None, 332928) 0
_________________________________________________________________
dense_3 (Dense) (None, 512) 170459648
_________________________________________________________________
dropout_4 (Dropout) (None, 512) 0
_________________________________________________________________
dense_4 (Dense) (None, 39) 20007
_________________________________________________________________
activation_1 (Activation) (None, 39) 0
=================================================================
Total params: 170,489,799
Trainable params: 170,489,799
Non-trainable params: 0
我将图表和变量保存在保护程序中。我故意将占位符命名为 X 和 Y，并将预测（这是一种操作）命名为：output。命名它们的目的是为了能够还原它们并用于预测。
我做了什么
- 为了构建和训练我的模型，我首先定义了占位符并构建了计算图。
- 然后，我定义了保存图形和变量的保护程序。
- 之后，我训练了我的网络（我使用了 1 个 epoch，因为我想尝试保存和恢复是否正常工作）。然后我将会话变量值保存在保护程序中并将保护程序导出到一个文件夹中，以便我以后可以恢复它。在关闭 tensorflow 会话之前。我计算了第一个 epoch 结束时的预测，我想将它与恢复会话的结果进行比较。
- 我恢复/重新加载了图表和变量（我在之前的操作中保存）。
- 我恢复了保存的占位符（X 和 Y）和操作“output”，以便使用与第一个训练时期的结束。 “目的是确保保存和恢复过程正常工作）。所以当我计算预测时，我得到了一个 None 值？我不明白我的方法有什么问题。
这是我使用的代码
定义和设计模型结构
def create_model(X,Y):
# Convolutional Layer #1
conv1 = tf.layers.conv2d(
inputs=X,
filters=32,
kernel_size=[3, 3],
padding="same",
activation=tf.nn.relu)
print('conv1 OUTPUT shape: ',conv1.shape)
# Dropout layer #1
dropout1 = tf.layers.dropout(
inputs=conv1, rate=0.2, training='TRAIN' == tf.estimator.ModeKeys.TRAIN)
print('drop1 OUTPUT shape: ',dropout1.shape)
# Convolutional Layer #2
conv2 = tf.layers.conv2d(
inputs=dropout1,
filters=32,
kernel_size=[3, 3],
padding="same",
activation=tf.nn.relu)
print('conv2 OUTPUT shape: ',conv2.shape)
# Pooling Layer #2
pool2 = tf.layers.max_pooling2d(inputs=conv2, pool_size=[2, 2],strides=2)
print('pool2 OUTPUT shape: ',pool2.shape)
pool2_flat = tf.reshape(pool2, [-1, pool2.shape[1]*pool2.shape[2]*pool2.shape[3]])
# Dense layer #3
dense3 = tf.layers.dense(inputs=pool2_flat, units=512, activation=tf.nn.relu)
print('dense3 OUTPUT shape: ',dense3.shape)
# Dropout layer #3
dropout3 = tf.layers.dropout(
inputs=dense3, rate=0.5, training='TRAIN' == tf.estimator.ModeKeys.TRAIN)
print('drop3 OUTPUT shape: ',dropout3.shape)
# Dense layer #4
Z = tf.layers.dense(inputs=dropout3, units=39, activation=tf.nn.sigmoid)
print('dense4 OUTPUT shape: ',Z.shape)
#Threshold
#Z1 = tf.to_int32(Z > 0.5)
#print('OUTPUT: shape output after threshold ',Z1.shape)
# Calculating cost
cost= tf.reduce_mean(Y * - tf.log(tf.clip_by_value(Z,1e-10,1.0)) + (1 - Y) * - tf.log(tf.clip_by_value(1 - Z,1e-10,1.0)),axis=0)
print('cost: shape of cost: ',cost.shape)
cost= tf.reshape(cost, [1, 39])
print('cost reshaped: shape of cost reshaped: ',cost.shape)
#Optimizer
optimizer = tf.train.AdamOptimizer(learning_rate=0.001).minimize(cost)
#Metric paramaters
predicted_positive = predicted_positives(Y,Z)
true_and_possible_positive = true_and_possible_positives(Y,Z)
#naming operations
output1=tf.multiply(Z,1,name='the_output_for_prediction')
cost1=tf.multiply(cost,1,name='cost')
return output1,predicted_positive,true_and_possible_positive,optimizer,cost1
在第一个 epoch 后创建模型、训练、保存和评估预测（工作正常）
# For a given (X_train,Y_train)&(X_valid,Y_train) and some hyperparam( some cannot be changed: it trains a model, saves it and returns the metric values.
#def train_model(X_train,Y_train,X_valid,Y_valid,num_epochs,minibatch_size=32):
#Reset tensorflow graph
ops.reset_default_graph()
# hyperparameters
num_epochs=1
minibatch_size=32
m=X_train.shape[0]
#Initialize metric variables
costs = []
precision=[]
recall=[]
costs_v = []
precision_v=[]
recall_v=[]
m=X_train.shape[0]
#Placeholders
X = tf.placeholder(tf.float32, [None, X_train.shape[1],X_train.shape[2],X_train.shape[3]],name="X")
Y = tf.placeholder(tf.float32, [None, Y_train.shape[1]],name="Y")
#Graph construction
output,predicted_positives,true_and_possible_positives,optimizer,cost=create_model(X,Y)
# Initializer for all the variables globally
init = tf.global_variables_initializer()
#Creating tf session and running it (i.e initialize the global variables)
sess=tf.Session()
sess.run(init)
# Create model saver
#model_saver = tf.train.Saver()
saver = tf.train.Saver()
#Training algorithm
#Running epochs
for epoch in range(num_epochs):
########################################### training ################################################
print('training epoch number :',epoch)
#initialize parameters of metrics
predicted_po=0
true_po=0
possible_po=0
#initialize cost param and mini_batches
minibatch_cost = 0.
num_minibatches = int(m / minibatch_size) # number of minibatches of size minibatch_size in the train set
minibatches = random_mini_batches(X_train, Y_train, minibatch_size)
#Running batches
for minibatch in minibatches:
#minibatch extraction
minibatch_X=X_train[minibatch,]
minibatch_Y=Y_train[minibatch,]
# IMPORTANT: The line that runs the graph on a minibatch.
# Run the session to execute the optimizer, cost and metric parameters , the feedict should contain a minibatch for (X,Y).
_ , temp_cost = sess.run([optimizer, cost], feed_dict={X:minibatch_X, Y:minibatch_Y})
predicted_p,true_p_p = sess.run([predicted_positives, true_and_possible_positives], feed_dict={X:minibatch_X, Y:minibatch_Y})
#calculate cost,predicted_true_possible positives per batch.
minibatch_cost += temp_cost / num_minibatches
predicted_po+=predicted_p
true_po+=true_p_p[0]
possible_po+=true_p_p[1]
#print batch param metric
#print('predicted_p per batch ',noi,' ',predicted_p)
#print('true_p per bartch ',noi,' ',true_p_p[0])
#print('possible_p per batch ',noi,' ',true_p_p[1])
'''
# Print the cost every epoch
# print ("Cost after epoch %i: %f" % (epoch, minibatch_cost))
print('precision_on_training epoch: ',epoch,' ',true_po/predicted_po)
print('recall on training epoch ',epoch,' ', true_po/possible_po)
'''
#appending tables of costs and metrics on training
costs.append(minibatch_cost)
precision.append(true_po/predicted_po)
recall.append(true_po/possible_po)
minibatches=None
minibatch_X=None
minibatch_Y=None
###################################### Validation ###################################################
print('validation epoch number :',epoch)
#create minibatches for validation
#initialize parameters of metrics
m_v=X_valid.shape[0]
minibatch_cost_v = 0.
num_minibatches_v = int(m_v / minibatch_size) # number of minibatches validation of size minibatch_size_v in the validation set
predicted_po_v=0
true_po_v=0
possible_po_v=0
minibatches_valid=random_mini_batches(X_valid, Y_valid, minibatch_size)
#running batches
for minibatch in minibatches_valid:
#print('batch validation number ', noi_v, ' / ',num_minibatches_v)
minibatch_X_valid=X_train[minibatch,]
minibatch_Y_valid=Y_train[minibatch,]
temp_cost_v = sess.run(cost, feed_dict={X:minibatch_X_valid, Y:minibatch_Y_valid})
predicted_p_v,true_p_p_v = sess.run([predicted_positives, true_and_possible_positives], feed_dict={X:minibatch_X_valid, Y:minibatch_Y_valid})
minibatch_cost_v += temp_cost_v / num_minibatches_v
predicted_po_v+=predicted_p_v
true_po_v+=true_p_p_v[0]
possible_po_v+=true_p_p_v[1]
'''
#Printing precision and recall on validation
print('precision_on_validation epoch: ',epoch,' ',true_po_v/predicted_po_v)
print('recall on validation epoch ',epoch,' ', true_po_v/possible_po_v)
'''
#appending tables of costs and metrics on training
costs_v.append(minibatch_cost_v)
precision_v.append(true_po_v/predicted_po_v)
recall_v.append(true_po_v/possible_po_v)
minibatches_valid=None
minibatch_X_valid=None
minibatch_Y_valid=None
#A simple test to check whether a restored model has exactly the same values (w and graph) as the original. Testing on a new output.
prediction_first_epoch=sess.run([output], feed_dict={X:X_train[[0]], Y:Y_train[[0]]})
print(np.mean(prediction_first_epoch))
#Save model
#global_step: used for checkpoints
saver.save(sess, 'ok/',global_step=1000)
##Save different training parameters.
costs_array=np.asarray(costs)
precision_array=np.asarray(precision)
recall_array=np.asarray(recall)
costs_v_array=np.asarray(costs_v)
precision_v_array=np.asarray(precision_v)
recall_v_array=np.asarray(recall_v)
np.save('costs_array', costs_array)
np.save('precision_array', precision_array)
np.save('recall_array', recall_array)
np.save('costs_v_array', costs_v_array)
np.save('precision_v_array', precision_v_array)
np.save('recall_v_array', recall_v_array)
sess.close()
恢复保存的模型并尝试使用与以前相同的数据进行预测
ops.reset_default_graph()
sess=tf.Session()
#Load meta_graph and restore weights
saver = tf.train.import_meta_graph('ok/-1000.meta')
saver.restore(sess,tf.train.latest_checkpoint('ok/'))
#put graph in variable and load adequate tensors & operations
graph = tf.get_default_graph()
X = graph.get_tensor_by_name("X:0")
Y = graph.get_tensor_by_name("Y:0")
output=graph.get_operation_by_name("the_output_for_prediction")
output_vis= sess.run(output, feed_dict={X:X_train[[0]], Y:Y_train[[0]]})
print(output_vis)
sess.close()
结果：
INFO:tensorflow:Restoring parameters from ok/-1000
None
问题
我不明白为什么我得到 None 值，即使我在默认图中恢复了保存的图并提取了两个占位符和预测操作。
【问题讨论】：
标签： python tensorflow machine-learning keras deep-learning