I've trained a TF Random Forest Classifier similar to the following code:
X = tf.placeholder(tf.float32, shape=[None, num_features])
Y = tf.placeholder(tf.int32, shape=[None])
hparams = tensor_forest.ForestHParams(num_classes=num_classes,
num_features=num_features,
num_trees=num_trees).fill()
forest_graph = tensor_forest.RandomForestGraphs(hparams)
train_op = forest_graph.training_graph(X, Y)
loss_op = forest_graph.training_loss(X, Y)
infer_op, _, _ = forest_graph.inference_graph(X)
correct_prediction = tf.equal(tf.argmax(infer_op, 1), tf.cast(Y,tf.int64))
accuracy_op = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
init_vars = tf.group(tf.global_variables_initializer(),
resources.initialize_resources(resources.shared_resources()))
with tf.Session() as sess:
sess.run(init_vars)
saver = tf.train.Saver()
for i in range(1, 100):
for batch_x, batch_y in render_batch(batch_size):
_, l = sess.run([train_op, loss_op], feed_dict={X: batch_x, Y: batch_y})
acc = sess.run(accuracy_op, feed_dict={X: batch_x, Y: batch_y})
print('Step %i, Loss: %f, Acc: %f' % (i, l, acc))
if acc >= 0.87:
print("Stopping and saving")
save_path = saver.save(sess, model_path)
print("Model saved in file: %s" % save_path)
break
Now I want to reload my model and use it to make predictions on unseen data like this:
with graph.as_default():
session_conf = tf.ConfigProto()
sess = tf.Session(config = session_conf)
with sess.as_default():
saver = tf.train.import_meta_graph("{}.meta".format(model_path))
saver.restore(sess,checkpoint_file)
accuracy_op = graph.get_operation_by_name("accuracy_op").outputs[0]
print(sess.run(accuracy_op, feed_dict={X: x_test, Y: y_test}))
However, I get the following error message:
KeyError: "The name 'accuracy_op' refers to an Operation not in the graph."
My question is - how can I save my model such that when I reload it, I can import those operations defined above and use them on unseen data?
Thanks!
Since you are using get_operation_by_name, you should have named the op accuracy_op. You can do it by using tf.identity:
accuracy_op = tf.identity(tf.reduce_mean(tf.cast(correct_prediction, tf.float32)), 'accuracy_op')
I see that you are using tensors X and Y without loading from the new graph. So name the tensors in the original code and then reload using the get_tensor_by_name()
Related
I am learning tensorflow from a DataCamp tutorial, at the final training phase I have this code below. My question is the line
opt = sess.run(optimizer, feed_dict={x: batch_x, y: batch_y})
why are we saving sess.run(optimizer, ...) as a variable? the variable "opt" isn't used again anywhere... some explanation as to how tensorflow's optimizer works under the hood would be awesome thanks!
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y))
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)
training_iters = 20
saver = tf.train.Saver()
# tf.reset_default_graph()
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
train_loss = []
test_loss = []
train_accuracy = []
test_accuracy = []
summary_writer = tf.summary.FileWriter('./Output', sess.graph)
for i in range(training_iters):
for batch in range(len(train_X)//batch_size):
batch_x = train_X[batch*batch_size:min((batch+1)*batch_size,len(train_X))]
batch_y = train_y[batch*batch_size:min((batch+1)*batch_size,len(train_y))]
opt = sess.run(optimizer, feed_dict={x: batch_x, y: batch_y})
# print(type(opt)) # class 'NoneType'
loss, acc = sess.run([cost, accuracy], feed_dict={x: batch_x, y: batch_y})
print("Iter " + str(i) + \
", Loss= " + "{:.6f}".format(loss) + \
", Training Accuracy= " + "{:.5f}".format(acc)) # prints the loss and training accuracy after each epoch
print("Optimization Finished!")
# Calculate accuracy for all 10000 mnist test images
test_acc, valid_loss = sess.run([accuracy,cost], feed_dict={x: val_X, y: val_y})
train_loss.append(loss)
test_loss.append(valid_loss)
train_accuracy.append(acc)
test_accuracy.append(test_acc)
print("Testing Accuracy:","{:.5f}".format(test_acc))
saved_path = saver.save(sess, './my-model', global_step=training_iters)
summary_writer.close()
i have this code to train a restore a model in tensorflow . but how can I make predictions.
def train_neural_network(x):
prediction=neural_network_model(x)
cost=tf.nn.softmax_cross_entropy_with_logits_v2(logits = prediction, labels = y)
optimizer=tf.train.AdamOptimizer(learning_rate=0.001).minimize(cost)
saver = tf.train.Saver()
with tf.Session() as sess:
#sess.run(tf.initialize_all_variables())
sess.run(tf.global_variables_initializer())
for epoch in range(hm_epochs):
epoch_loss = 0
i = 0
#while i < len(train_x):
t = len(train_x)
f = t%batch_size
while i < (t-f):
start = i
end = i+batch_size
batch_x = np.array(train_x[start:end])
batch_y = np.array(train_y[start:end])
_, c = sess.run([optimizer, cost], feed_dict={x: batch_x, y: batch_y})
epoch_loss += c
#epoch_loss = epoch_loss + c
i+=batch_size
#i = i + batch_size
print('Epoch =', epoch+1, '/',hm_epochs,'loss:',epoch_loss)
save_path = saver.save(sess, "sesionestensorflow/model1.ckpt")
print("Model saved in path: %s" % save_path)
correct = tf.equal(tf.argmax(prediction, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct, 'float'))
print('Accuracy:',accuracy.eval({x:test_x, y:test_y}))
i see this answer but i can`t make the prediction.
Using saved model for prediction in tensorflow
You just have to create an empy graph, define the network, load the saved weights and thus run inference.
prediction = tf.argmax(neural_network_model(x), 1)
saver = tf.train.Saver()
with tf.Session() as sess:
# load the trained weights into the model
saver.restore(sess, "sesionestensorflow/model1.ckpt")
# just use the model
out = sess.run(prediction, feed_dict={x: <your input> })
it is a neural network of 12 layer full connect and 2 layer CNN. I have a sample to training it and want to save the model during the training process.But when I want to use the saving model to predict my new sample, I find that the model didn't save the whole variable.
Here are apart of my save model code.
W_hidden_4 = tf.Variable(weight_initializer([n_neurons_3,n_neurons_4]))
bias_hidden_4 = tf.Variable(bias_initializer([n_neurons_4]))
hidden_4 = tf.nn.relu(tf.add(tf.matmul(hidden_3,W_hidden_4),bias_hidden_4))
# Output layer: Variables for output weights and biases
W_out = tf.Variable(weight_initializer([n_neurons_6,n_rot]),name="W_out")
bias_out = tf.Variable(bias_initializer([n_rot]),name="bias_out")
out = tf.add(tf.matmul(hiddens['hidden_14'], W_out), bias_out,name="out")
# Cost function
# tf.reduce_mean:count the average value
mse = tf.reduce_mean(tf.squared_difference(out, Y))
opt = tf.train.AdamOptimizer().minimize(mse)
# Run initializer
net.run(tf.global_variables_initializer())
for e in range(epochs):
print(str(e) + ":")
# Shuffle training data
shuffle_indices = np.random.permutation(np.arange(len(y_train)))
X_train = X_train[shuffle_indices]
y_train = y_train[shuffle_indices]
# Minibatch training
for i in range(0, len(y_train) // batch_size):
start = i * batch_size
batch_x = X_train[start:start + batch_size]
batch_y = y_train[start:start + batch_size]
# Run optimizer with batch
net.run(opt, feed_dict={X: batch_x, Y: batch_y})
# Show progress
if np.mod(i, 50) == 0:
# Prediction
pred = net.run(out, feed_dict={X: X_test})
mse_final = net.run(mse, feed_dict={X: batch_x, Y: batch_y})
print(mse_final)
if e%50 == 0:
model_path = "/home/student/fulldata/src/fc_bigpara/model_" + str(e/50)
save_path = saver.save(net, model_path)
and the following is my restore code
X_test = np.loadtxt('test_x.txt')
sess = tf.Session()
# First let's load meta graph and restore weights
saver =tf.train.import_meta_graph('/home/student/fulldata/src/old_model/fc_bigpara_14/model_19.0.meta')
#all_vars = tf.trainable_variables()
#for v in all_vars:
# print(v.name)
#print v.name,v.eval(self.sess)
saver.restore(sess,
"/home/student/fulldata/src/old_model/fc_bigpara_14/model_19.0")
all_vars = tf.trainable_variables()
for v in all_vars:
#print(v.name, v.eval(sess))
print(v.name)
print(v.shape)
# Now, let's access and create placeholders variables and
# create feed-dict to feed new data
graph = tf.get_default_graph()
X = tf.placeholder(dtype=tf.float32, shape=[None, 3])
Y = tf.placeholder(dtype=tf.float32, shape=[None, 6])
out=graph.get_tensor_by_name("Variable_25:0")
#todo
with open("result.txt","w") as f:
#for i in range(0, len(X_test)):
#start=i*batch_size
#batch_x = X_test[start:start + batch_size]
#batch_x=X_test[i]
feed_dict={X:X_test}
result=sess.run(out,feed_dict)
#print(result.shape)
I can't find the parameter "out" in the model't variable and I have add "name ='out'" but it can't work.So I can't run the code following
result=sess.run(out,feed_dict)
how can I modify my code to fix the bug??
I have implemented a NN for MNIST using TensorFlow. I want to show the result on the TensorBoard. Flowing are screenshots of the TensorBoard that I have implemented. But the IMAGES page shows "No image data was found".
What information should be shown here? I should ignore it?
CODE
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
tf.reset_default_graph()
mnist = input_data.read_data_sets('data', one_hot=True)
batch_size = 100
learning_rate = 0.5
training_epochs = 5
logs_path = "C:/tmp/mlp"
with tf.name_scope('input'):
x = tf.placeholder(tf.float32, shape=[None, 784], name="x-input")
y_ = tf.placeholder(tf.float32, shape=[None, 10], name="y-input")
with tf.name_scope("weights"):
W = tf.Variable(tf.zeros([784, 10]))
with tf.name_scope("biases"):
b = tf.Variable(tf.zeros([10]))
with tf.name_scope("softmax"):
y = tf.nn.softmax(tf.matmul(x, W) + b)
with tf.name_scope('cross_entropy'):
cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y), reduction_indices=[1]))
with tf.name_scope('train'):
train_op = tf.train.GradientDescentOptimizer(learning_rate).minimize(cross_entropy)
with tf.name_scope('Accuracy'):
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
tf.summary.scalar("cost", cross_entropy)
tf.summary.scalar("accuracy", accuracy)
summary_op = tf.summary.merge_all()
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
summary_writer = tf.summary.FileWriter("C:/tmp/mlp", sess.graph)
for epoch in range(training_epochs):
batch_count = int(mnist.train.num_examples / batch_size)
for i in range(batch_count):
batch_x, batch_y = mnist.train.next_batch(batch_size)
_, summary = sess.run([train_op, summary_op], feed_dict={x: batch_x, y_: batch_y})
summary_writer.add_summary(summary, epoch * batch_count + i)
if epoch % 5 == 0:
print("Epoch: ", epoch)
print("Accuracy: ", accuracy.eval(feed_dict={x: mnist.test.images, y_: mnist.test.labels}))
print("done")
The only lines in your code that refer to a summary operation are:
tf.summary.scalar("cost", cross_entropy)
tf.summary.scalar("accuracy", accuracy)
These lines create 2 scalar summaries (and add the created summary to a default collection that contains every defined summary).
You're not defining any image summary (with tf.summmary.image) thus that tab in tensorboard will be empty.
Just ignore them, Because you did save any tf.summary.image summary, Tensorboard won't show anything in this tab;
I know that the following is an already answered question, but even though i tried and tried all the proposed solutions, none of them solved my problem.
I made this net for training over MNIST dataset. At the beginning it was deeper, but in order to focus on the problem i simplified it.
mnist = mnist_data.read_data_sets('MNIST_data', one_hot=True)
# train the net
def train():
for i in range(1000):
batch_xs, batch_ys = mnist.train.next_batch(100)
sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})
print("accuracy", sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels}))
if i%100==0:
save_path = saver.save(sess, "./tmp/model.ckpt", global_step = i, write_meta_graph=True)
print("Model saved in file: %s" % save_path)
# evaluate the net
def test(image, label):
true_value = tf.argmax(label, 1)
prediction = tf.argmax(y, 1)
print("true value:", sess.run(true_value))
print("predictions", sess.run(prediction, feed_dict={x:image}))
sess = tf.InteractiveSession()
x = tf.placeholder("float", shape=[None, 784])
W = tf.Variable(tf.zeros([784,10]), name = "W1")
b = tf.Variable(tf.zeros([10]), name = "B1")
y = tf.nn.softmax(tf.matmul(x,W) + b, name ="Y")
y_ = tf.placeholder("float", shape=[None, 10])
cross_entropy = -tf.reduce_sum(y_*tf.log(y))
train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)
correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
saver = tf.train.Saver()
model_to_restore="./tmp/model.ckpt-100.meta"
if os.path.isfile(model_to_restore):
#what i have to do here?????#
else:
#this part works!#
print("Model does not exist: training")
train()
Thanks everybody for the answers!
Regards,
Silvio
UPDATE
I tried both
saver.restore(sess, model_to_restore)
and
saver = tf.train.import_meta_graph(model_to_restore)
saver.restore(sess, model_to_restore)
but in both cases i had this error from terminal:
DataLossError (see above for traceback): Unable to open table file ./tmp/model.ckpt.meta: Data loss: not an sstable (bad magic number): perhaps your file is in a different file format and you need to use a different restore operator?
[[Node: save/RestoreV2 = RestoreV2[dtypes=[DT_FLOAT], _device="/job:localhost/replica:0/task:0/cpu:0"](_recv_save/Const_0, save/RestoreV2/tensor_names, save/RestoreV2/shape_and_slices)]]
I think your location to the model might be wrong and i would advise you to give the following workflow a try.
Since the saved models comprise several files i usually save them to a folder after training:
modelPath = "myMNIST/model"
saved_path = saver.save(sess, os.path.join(modelPath, "model.ckpt"))
print("Model saved in file: ", saved_path)
This will also tell you the exact location where it has been saved.
Then i can start my predictor inside the saved location (cd into myMNIST) and restore the model by:
ckpt = tf.train.get_checkpoint_state("./model")
if ckpt and ckpt.model_checkpoint_path:
print("Restored Model")
saver.restore(sess, ckpt.model_checkpoint_path)
else:
print("Could not restore model!")