I'm trying to restore a model in Tensorflow which I've trained. The problem is that it does not seem like the weights are properly restored.
For the training I've got the weights and biases defined as:
W = {
'h1': tf.Variable(tf.random_normal([n_inputs, n_hidden_1]), name='wh1'),
'h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2]), name='wh2'),
'o': tf.Variable(tf.random_normal([n_hidden_2, n_classes]), name='wo')
}
b = {
'b1': tf.Variable(tf.random_normal([n_hidden_1]), name='bh1'),
'b2': tf.Variable(tf.random_normal([n_hidden_2]), name='bh2'),
'o': tf.Variable(tf.random_normal([n_classes]), name='bo')
}
Then I do some training on my own custom 2D image dataset and save the model by calling the tf.saver
saver = tf.train.Saver()
saver.save(sess, 'tf.model')
Later I want to restore that model with the exact same weights, so I build the model as before (also with the random_normal initialization) and call the tf.saver.restore
saver = tf.train.import_meta_graph('tf.model.meta')
saver.restore(sess, tf.train.latest_checkpoint('./'))
Now, if i call:
temp = sess.run(W['h1'][0][0])
print temp
I get random values, and not the restored value of the weight.
I've drawn a blank on this one, can somebody point me in the right direction?
FYI, I've tried (without) luck to simply declare the tf.Variables, but I keep getting:
ValueError: initial_value must be specified.
even though Tensorflow themselves state that it should be possible to simply declare with no initial value (https://www.tensorflow.org/programmers_guide/variables part: Restoring Values)
Update 1
When I, as suggested, run
all_vars = tf.global_variables()
for v in all_vars:
print v.name
I get the following output:
wh1:0
wh2:0
wo:0
bh1:0
bh2:0
bo:0
wh1:0
wh2:0
wo:0
bh1:0
bh2:0
bo:0
beta1_power:0
beta2_power:0
wh1/Adam:0
wh1/Adam_1:0
wh2/Adam:0
wh2/Adam_1:0
wo/Adam:0
wo/Adam_1:0
bh1/Adam:0
bh1/Adam_1:0
bh2/Adam:0
bh2/Adam_1:0
bo/Adam:0
bo/Adam_1:0
Which shows that the variables indeed is read. However invoking
print sess.run("wh1:0")
Results in the error: Attempting to use uninitialized value wh1
So with the help of you guys, I ended up dividing the saving and restoring parts of my program into two files, to ensure that no unwanted variables were initialized.
Train and Save routines fnn.py
def build(self, topology):
"""
Builds the topology of the model
"""
# Sanity check
assert len(topology) == 4
n_inputs = topology[0]
n_hidden_1 = topology[1]
n_hidden_2 = topology[2]
n_classes = topology[3]
# Sanity check
assert self.img_h * self.img_w == n_inputs
# Instantiate TF Placeholders
self.x = tf.placeholder(tf.float32, [None, n_inputs], name='x')
self.y = tf.placeholder(tf.float32, [None, n_classes], name='y')
self.W = {
'h1': tf.Variable(tf.random_normal([n_inputs, n_hidden_1]), name='wh1'),
'h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2]), name='wh2'),
'o': tf.Variable(tf.random_normal([n_hidden_2, n_classes]), name='wo')
}
self.b = {
'b1': tf.Variable(tf.random_normal([n_hidden_1]), name='bh1'),
'b2': tf.Variable(tf.random_normal([n_hidden_2]), name='bh2'),
'o': tf.Variable(tf.random_normal([n_classes]), name='bo')
}
# Create model
self.l1 = tf.nn.sigmoid(tf.add(tf.matmul(self.x, self.W['h1']), self.b['b1']))
self.l2 = tf.nn.sigmoid(tf.add(tf.matmul(self.l1, self.W['h2']), self.b['b2']))
logits = tf.add(tf.matmul(self.l2, self.W['o']), self.b['o'])
# Define predict operation
self.predict_op = tf.argmax(logits, 1)
probs = tf.nn.softmax(logits, name='probs')
# Define cost function
self.cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits, self.y))
# Adding these to collection so we can restore them again
tf.add_to_collection('inputs', self.x)
tf.add_to_collection('inputs', self.y)
tf.add_to_collection('outputs', logits)
tf.add_to_collection('outputs', probs)
tf.add_to_collection('outputs', self.predict_op)
def train(self, X, Y, n_epochs=10, learning_rate=0.001, logs_path=None):
"""
Trains the Model
"""
self.optimizer = tf.train.AdamOptimizer(learning_rate).minimize(self.cost)
costs = []
# Instantiate TF Saver
saver = tf.train.Saver()
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
sess.run(tf.local_variables_initializer())
# start the threads used for reading files
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
# Compute total number of batches
total_batch = int(self.get_num_examples() / self.batch_size)
# start training
for epoch in range(n_epochs):
for i in range(total_batch):
batch_xs, batch_ys = sess.run([X, Y])
# run the training step with feed of images
_, cost = sess.run([self.optimizer, self.cost], feed_dict={self.x: batch_xs,
self.y: batch_ys})
costs.append(cost)
print "step %d" % (epoch * total_batch + i)
#costs.append(cost)
print "Epoch %d" % epoch
saver.save(sess, self.model_file)
temp = sess.run(self.W['h1'][0][0])
print temp
if self.visu:
plt.plot(costs)
plt.show()
# finalize
coord.request_stop()
coord.join(threads)
Predict routine fnn_eval.py:
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
sess.run(tf.local_variables_initializer())
g = tf.get_default_graph()
# restore the model
self.saver = tf.train.import_meta_graph(self.model_file)
self.saver.restore(sess, tf.train.latest_checkpoint('./tfmodels/fnn/'))
wh1 = g.get_tensor_by_name("wh1:0")
print sess.run(wh1[0][0])
x, y = tf.get_collection('inputs')
logits, probs, predict_op = tf.get_collection('outputs')
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
predictions = []
print Y.eval()
for i in range(1):#range(self.get_num_examples()):
batch_xs = sess.run(X)
# Reshape batch_xs if only a single image is given
# (numpy is 4D: batch_size * heigth * width * channels)
batch_xs = np.reshape(batch_xs, (-1, self.img_w * self.img_h))
prediction, probabilities, logit = sess.run([predict_op, probs, logits], feed_dict={x: batch_xs})
predictions.append(prediction[0])
# finalize
coord.request_stop()
coord.join(threads)
I guess the problem might be caused by creating a new variable when you restore the model, not getting the already existed variable. I tried this code
saver = tf.train.import_meta_graph('./model.ckpt-10.meta')
w1 = None
for v in tf.global_variables():
print v.name
w1 = tf.get_variable('wh1', [])
init = tf.global_variables_initializer()
sess.run(init)
saver.restore(sess, './model.ckpt-10')
for v in tf.global_variables():
print v.name
and clearly you can see the output that it creates a new variable called wh1_1:0.
If you try this
w1 = None
for v in tf.global_variables():
print v.name
if v.name == 'wh1:0':
w1 = v
init = [tf.global_variables_initializer(), tf.local_variables_initializer()]
sess.run(init)
saver.restore(sess, './model.ckpt-10')
for v in tf.global_variables():
print v.name
temp = sess.run(w1)
print temp[0][0]
There will be no problem.
Tensorflow suggests that it is better to use tf.variable_scope() (link) like this
with tf.variable_scope("foo"):
v = tf.get_variable("v", [1])
with tf.variable_scope("foo", reuse=True):
v1 = tf.get_variable("v", [1])
assert v1 == v
I have meet the same problem when saving model to saved_model format. Anyone using the function add_meta_graph_and_variables to save the model for serving, be careful about this parameter "legacy_init_op: Legacy support for op or group of ops to execute after the restore op upon a load."
You want to pass in a var_list to the Saver.
In your case, the variable list would come from your W and b dictionaries: var_list = list(W.values())+list(b.values()). Then, to restore the model, pass in var_list to the Saver: saver = tf.train.Saver(var_list=var_list).
Next, you need to get your checkpoint state: model = tf.train.get_checkpoint_state(<your saved model directory>). After that you can restore the trained weights.
var_list = list(W.values())+list(b.values())
saver = tf.train.Saver(var_list=var_list)
model = tf.get_checkpoint_state('./model/')
with tf.Session() as sess:
saver.restore(sess,model.model_checkpoint_path)
#Now use the pretrained weights
Related
I'm a beginner in Tensorflow and I found this neural network for binary classification which is giving me decent results, I would like to know after I run the session how can I save the model? I already try from the official website but nothing is working.
class AnnMLP():
def train(self,X_input,y_input,test,range_iteration,learning_rate): X = tf.compat.v1.placeholder(tf.float32, [None,27]) Y = tf.compat.v1.placeholder(tf.float32, [None,1])
# input
W1 = tf.Variable(tf.random.normal([27,60], seed=0), name='weight1')
b1 = tf.Variable(tf.random.normal([60], seed=0), name='bias1')
layer1 = tf.nn.sigmoid(tf.matmul(X,W1) + b1)
dropout_layer = keras.layers.Dropout(rate=0.4)
layer1=dropout_layer(layer1)
# hidden1
W2 = tf.Variable(tf.random.normal([60,60], seed=0), name='weight2')
b2 = tf.Variable(tf.random.normal([60], seed=0), name='bias2')
layer2 = tf.nn.sigmoid(tf.matmul(layer1,W2) + b2)
dropout_layer = keras.layers.Dropout(rate=0.4)
layer2=dropout_layer(layer2)
# hidden2
W3 = tf.Variable(tf.random.normal([60,90], seed=0), name='weight3')
b3 = tf.Variable(tf.random.normal([90], seed=0), name='bias3')
layer3 = tf.nn.sigmoid(tf.matmul(layer2,W3) + b3)
dropout_layer = keras.layers.Dropout(rate=0.4)
layer3=dropout_layer(layer3)
# output
W4 = tf.Variable(tf.random.normal([90,1], seed=0), name='weight4')
b4 = tf.Variable(tf.random.normal([1], seed=0), name='bias4')
logits = tf.matmul(layer3,W4) + b4
hypothesis = tf.nn.sigmoid(logits)
cost_i = tf.nn.sigmoid_cross_entropy_with_logits(logits=logits,labels=Y)
cost = tf.reduce_mean(cost_i)
train =tf.train.GradientDescentOptimizer(learning_rate=learning_rate).minimize(cost)
#train = tf.train.AdamOptimizer(learning_rate=0.0001).minimize(cost) train = tf.train.AdadeltaOptimizer(learning_rate=learning_rate).minimize(cost)
prediction = tf.cast(hypothesis > 0.5, dtype=tf.float32)
correct_prediction = tf.equal(prediction, Y)
accuracy = tf.reduce_mean(tf.cast(correct_prediction, dtype=tf.float32))
print("\n============Processing============")
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for step in range(range_iteration):
sess.run(train, feed_dict={X: X_input, Y: y_input})
if step % 1000 == 0:
loss, acc = sess.run([cost, accuracy], feed_dict={X: X_input, Y: y_input})
print("Step: {:5}\tLoss: {:.3f}\tAcc: {:.2%}".format(step, loss, acc))
train_acc = sess.run(accuracy, feed_dict={X: X_input, Y: y_input})
if test == True:
test_acc,test_predict,test_correct = sess.run([accuracy,prediction,correct_prediction], feed_dict={X: X_test, Y: y_test})
return test_predict
Tensorflow provides privilege to load and restore entire model and checkpoints. For more details find the link here.
Tensorflow < 2
saver = tf.train.Saver()
with tf.Session() as sess:
sess.run()
# Do some work with the model.
# Save the variables to disk.
save_path = saver.save(sess, "/tmp/model.ckpt")
print("Model saved in path: %s" % save_path)
Tensorflow > 2
# Create and train a new model instance.
model = create_model()
model.fit(train_images, train_labels, epochs=10)
# Save the entire model to a HDF5 file.
model.save('my_model.h5')
Tensorflow provides privilege to load and restore entire model and checkpoints
Tensorflow < 2
saver = tf.train.Saver()
with tf.Session() as sess:
sess.run()
# Do some work with the model.
# Save the variables to disk.
save_path = saver.save(sess, "/tmp/model.ckpt")
print("Model saved in path: %s" % save_path)
Tensorflow>2
# Create and train a new model instance.
model = create_model()
model.fit(train_images, train_labels, epochs=10)
# Save the entire model to a HDF5 file.
model.save('my_model.h5'
I am training two sub-models in parallel, and want to average them after both of them are finished.
The model is implemented out of tensor2tensor (but still use tensorflow). Part of the definition is like this:
def build_model(self):
layer_sizes = [n,n,n]
kernel_sizes = [n,n,n]
tf.reset_default_graph()
self.graph = tf.Graph()
with self.graph.as_default():
self.num_classes = num_classes
# placeholder for parameter
self.learning_rate = tf.placeholder(tf.float32, name="learning_rate")
self.dropout_keep_prob = tf.placeholder(tf.float32, name="dropout_keep_prob")
self.phase = tf.placeholder(tf.bool, name="phase")
# Placeholders for regular data
self.input_x = tf.placeholder(tf.float32, [None, None, input_feature_dim], name="input_x")
self.input_y = tf.placeholder(tf.float32, [None, num_classes], name="input_y")
h = self.input_x
......[remaining codes]
I save it following:
def save_model(sess, output):
saver = tf.train.Saver()
save_path = saver.save(sess, os.path.join(output, 'model'))
When I load it, I use:
def load_model(self, sess, input_dir, logger):
if logger is not None:
logger.info("Start loading graph ...")
saver = tf.train.import_meta_graph(os.path.join(input_dir, 'model.meta'))
saver.restore(sess, os.path.join(input_dir, 'model'))
self.graph = sess.graph
self.input_x = self.graph.get_tensor_by_name("input_x:0")
self.input_y = self.graph.get_tensor_by_name("input_y:0")
self.num_classes = self.input_y.shape[1]
self.learning_rate = self.graph.get_tensor_by_name("learning_rate:0")
self.dropout_keep_prob = self.graph.get_tensor_by_name("dropout_keep_prob:0")
self.phase = self.graph.get_tensor_by_name("phase:0")
self.loss = self.graph.get_tensor_by_name("loss:0")
self.optimizer = self.graph.get_operation_by_name("optimizer")
self.accuracy = self.graph.get_tensor_by_name("accuracy/accuracy:0")
I use the avg_checkpoint to average two sub-models:
python utils/avg_checkpoints.py
--checkpoints path/to/checkpoint1,path/to/checkpoint2
--num_last_checkpoints 2
--output_path where/to/save/the/output
But I find problems when I check the avg_checkpoints code further:
for checkpoint in checkpoints:
reader = tf.train.load_checkpoint(checkpoint)
for name in var_values:
tensor = reader.get_tensor(name)
var_dtypes[name] = tensor.dtype
var_values[name] += tensor
tf.logging.info("Read from checkpoint %s", checkpoint)
for name in var_values: # Average.
var_values[name] /= len(checkpoints)
with tf.variable_scope(tf.get_variable_scope(), reuse=tf.AUTO_REUSE):
tf_vars = [
tf.get_variable(v, shape=var_values[v].shape, dtype=var_dtypes[v])
for v in var_values
]
placeholders = [tf.placeholder(v.dtype, shape=v.shape) for v in tf_vars]
assign_ops = [tf.assign(v, p) for (v, p) in zip(tf_vars, placeholders)]
global_step = tf.Variable(
0, name="global_step", trainable=False, dtype=tf.int64)
saver = tf.train.Saver(tf.all_variables())
# Build a model consisting only of variables, set them to the average values.
with tf.Session() as sess:
for p, assign_op, (name, value) in zip(placeholders, assign_ops,
six.iteritems(var_values)):
sess.run(assign_op, {p: value})
# Use the built saver to save the averaged checkpoint.
saver.save(sess, FLAGS.output_path, global_step=global_step)
It only save the variables, not all tensors. E.G. when I load it using the above load_model function, it cannot have "input_x:0" tensor. Is this script wrong, or I should modify it based on my use?
I am using TF r1.13. Thanks!
I've trained my NN in Tensorflow and saved the model like this:
def neural_net(x):
layer_1 = tf.layers.dense(inputs=x, units=195, activation=tf.nn.sigmoid)
out_layer = tf.layers.dense(inputs=layer_1, units=6)
return out_layer
train_x = pd.read_csv("data_x.csv", sep=" ")
train_y = pd.read_csv("data_y.csv", sep=" ")
train_x = train_x / 6 - 0.5
train_size = 0.9
train_cnt = int(floor(train_x.shape[0] * train_size))
x_train = train_x.iloc[0:train_cnt].values
y_train = train_y.iloc[0:train_cnt].values
x_test = train_x.iloc[train_cnt:].values
y_test = train_y.iloc[train_cnt:].values
x = tf.placeholder("float", [None, 386])
y = tf.placeholder("float", [None, 6])
nn_output = neural_net(x)
cost = tf.reduce_mean(tf.losses.mean_squared_error(labels=y, predictions=nn_output))
optimizer = tf.train.AdamOptimizer(learning_rate=0.001).minimize(cost)
training_epochs = 5000
display_step = 1000
batch_size = 30
keep_prob = tf.placeholder("float")
saver = tf.train.Saver()
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for epoch in range(training_epochs):
total_batch = int(len(x_train) / batch_size)
x_batches = np.array_split(x_train, total_batch)
y_batches = np.array_split(y_train, total_batch)
for i in range(total_batch):
batch_x, batch_y = x_batches[i], y_batches[i]
_, c = sess.run([optimizer, cost],
feed_dict={
x: batch_x,
y: batch_y,
keep_prob: 0.8
})
saver.save(sess, 'trained_model', global_step=1000)
Now I want to use the trained model in a different file. Of course there are many many examples of restoring and saving the model, I went through lots of them. Still I couldn't make any of them work, there is always some kind of error. So this is my restore file, could you please help me to make it restore the saved model?
saver = tf.train.import_meta_graph('trained_model-1000.meta')
y_pred = []
with tf.Session() as sess:
saver.restore(sess, tf.train.latest_checkpoint('./'))
sess.run([y_pred], feed_dict={x: input_values})
E.g. this attempt gave me the error "The session graph is empty. Add operations to the graph before calling run()." So what operation should I add to the graph and how? I don't know what that operation should be in my model... I don't understand this whole concept of saving/restoring in Tensorflow. Or should I do the restoring completely differently? Thanks in advance!
Forgive me if I am wrong but tf.train.Saver() only saves the variable values not the graph itself. This means that if you want to load the model in a different file you need to rebuild the graph or somehow load the graph as well. Tensorflow documentation states:
The tf.train.Saver object not only saves variables to checkpoint files, it also restores variables. Note that when you restore variables from a file you do not have to initialize them beforehand.
Consider the following example:
One file that saves the model:
# Create some variables.
v1 = tf.get_variable("v1", shape=[3], initializer = tf.zeros_initializer)
v2 = tf.get_variable("v2", shape=[5], initializer = tf.zeros_initializer)
inc_v1 = v1.assign(v1+1)
dec_v2 = v2.assign(v2-1)
# Add an op to initialize the variables.
init_op = tf.global_variables_initializer()
# Add ops to save and restore all the variables.
saver = tf.train.Saver()
# Later, launch the model, initialize the variables, do some work, and save the
# variables to disk.
with tf.Session() as sess:
sess.run(init_op)
# Do some work with the model.
inc_v1.op.run()
dec_v2.op.run()
# Save the variables to disk.
save_path = saver.save(sess, "/tmp/model.ckpt")
print("Model saved in file: %s" % save_path)
The other file that loads the previously saved model:
tf.reset_default_graph()
# Create some variables.
v1 = tf.get_variable("v1", shape=[3])
v2 = tf.get_variable("v2", shape=[5])
# Add ops to save and restore all the variables.
saver = tf.train.Saver()
# Later, launch the model, use the saver to restore variables from disk, and
# do some work with the model.
with tf.Session() as sess:
# Restore variables from disk.
saver.restore(sess, "/tmp/model.ckpt")
print("Model restored.")
# Check the values of the variables
print("v1 : %s" % v1.eval())
print("v2 : %s" % v2.eval())
output = sess.run(nn_output, feed_dict={ x: batch_x, keep_prob: 0.8 })
Where nn_output is the name is the output variable of the last layer of you network. You can save you variable using:
saver = tf.train.Saver([nn_output])
saver.save(sess, 'my_test_model',global_step=1000) # save every 1000 steps
and therefore in your code:
out_layer = tf.layers.dense(inputs=layer_1, units=6)
should be :
out_layer = tf.layers.dense(inputs=layer_1, units=6, name='nn_output')
To restore:
with tf.Session() as sess:
saver = tf.train.import_meta_graph('my_test_model')
saver.restore(sess,tf.train.latest_checkpoint('./'))
Now you should have access to that node of the graph. If the name is not specified, it is difficult to recover that particular layer.
You can know use tf.saved_model.builder.SavedModelBuilder function.
The main lines for the saving:
builder = tf.saved_model.builder.SavedModelBuilder(graph_location)
builder.add_meta_graph_and_variables(sess, ["cnn_mnist"])
builder.save()
A code to save the model :
...
def main(_):
# Import data
mnist = input_data.read_data_sets(FLAGS.data_dir)
# Create the model
x = tf.placeholder(tf.float32, [None, 784])
# Define loss and optimizer
y_ = tf.placeholder(tf.int64, [None])
# Build the graph for the deep net
y_conv, keep_prob = deepnn(x) # an unknow model model
with tf.name_scope('loss'):
cross_entropy = tf.losses.sparse_softmax_cross_entropy(
labels=y_, logits=y_conv)
cross_entropy = tf.reduce_mean(cross_entropy)
with tf.name_scope('adam_optimizer'):
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
with tf.name_scope('accuracy'):
correct_prediction = tf.equal(tf.argmax(y_conv, 1), y_)
correct_prediction = tf.cast(correct_prediction, tf.float32)
accuracy = tf.reduce_mean(correct_prediction)
graph_location ="tmp/"
print('Saving graph to: %s' % graph_location)
**builder = tf.saved_model.builder.SavedModelBuilder(graph_location)**
train_writer = tf.summary.FileWriter(graph_location)
train_writer.add_graph(tf.get_default_graph())
saver = tf.train.Saver(max_to_keep=1)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
**builder.add_meta_graph_and_variables(sess, ["cnn_mnist"])**
for i in range(20000):
batch = mnist.train.next_batch(50)
if i % 100 == 0:
train_accuracy = accuracy.eval(feed_dict={
x: batch[0], y_: batch[1], keep_prob: 1.0})
print('step %d, training accuracy %g' % (i, train_accuracy))
train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})
print('test accuracy %g' % accuracy.eval(feed_dict={
x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0}))
**builder.save()**
saver.save(sess, "tmp/my_checkpoint.ckpt", global_step =0)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--data_dir', type=str,
default='/tmp/tensorflow/mnist/input_data',
help='Directory for storing input data')
FLAGS, unparsed = parser.parse_known_args()
tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)
`
A code to restore the model :
import tensorflow as tf
# récupération des poids
export_dir = "tmp"
sess = tf.Session()
tf.saved_model.loader.load(sess,["cnn_mnist"], export_dir)
#trainable_var = tf.trainable_variables()
trainable_var = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES)
for var in trainable_var:
print(var.name)`
This question is old. But if someone else is struggling with doing predictions with a trained model (with TF 1.x) this code might help.
Pay attention that
Your network construction/defining code must be executed before the Saver() instance is created. Otherwise you get the error: ValueError: No variables to save. In the code below the LeNet(x) method constructs the network for input placeholder x.
You should not initialize the variables in the session. Because obviously you are loading them from the saved model.
# all the network construction code
# (e.g. defining the variables and layers)
# must be exectured before the creation of
# the Saver() object. Otherwise you get the
# error: ValueError: No variables to save.
logits = LeNet(x)
saver = tf.train.Saver()
index = random.randint(0, len(X_train))
image = X_train[index].squeeze()
label = y_train[index]
print("Label: ", label)
plt.figure(figsize=(1,1))
plt.imshow(image, cmap="gray")
plt.show()
with tf.Session() as sess:
saver.restore(sess, tf.train.latest_checkpoint('./checkpoints/'))
logits_output = sess.run(logits, feed_dict={x: image.reshape((1, 32, 32, 1))})
logits_output = logits_output.squeeze()
pred_output = np.exp(logits_output)/sum(np.exp(logits_output)) #softmax
print("Logits: ", logits_output)
print("Prediction output:", pred_output)
print("Predicted Label: ", np.argmax(pred_output))
I want to train a model once in tensorflow, and then want to use the trained model for predicting some functions. Before we get into specifics, lets define a couple of functions ...
def runTF(func, inpDict):
sess = tf.Session()
init = tf.global_variables_initializer()
sess.run(init)
result = sess.run(func, feed_dict = inpDict)
sess.close()
return result
and
def optTF(opt, res, others, inpDict, nSteps, printSteps=50):
os, re = [], []
sess = tf.Session()
init = tf.global_variables_initializer()
sess.run(init)
for i in range(nSteps):
# First run the optimizer ...
sess.run(opt, feed_dict = inpDict)
# Save all the data you want to save
temp = sess.run( [res] + others, feed_dict = inpDict)
re.append(temp[0])
os.append(temp[1:])
if (i%printSteps) == 0:
print('{:5d}'.format(i))
sess.close()
return re, os
Here are a couple of steps for what I am doing ...
A. Generating some data
N = 500
features = 2
nSteps = 1000
X = np.array([np.random.random(N), np.random.random(N)])
data = [X.T, X[0].reshape(-1, 1)]
B. create a simple linear model
d = tf.placeholder(shape = np.shape(data[0]), dtype = tf.float32, name='d') # input layer
dOut = tf.placeholder(shape = np.shape(data[1]), dtype = tf.float32, name='dOut') # output layer
W = tf.Variable( np.random.randn(features, 1), dtype = tf.float32, name='W')
b = tf.Variable( np.zeros((1,1)), dtype = tf.float32, name='b')
result = tf.matmul(d, W)+b
cost = tf.reduce_mean((dOut - result)**2)
optimizer = tf.train.AdamOptimizer(learning_rate = 0.1).minimize(cost)
C. Lets run the optimiser
inpDict = {d: data[0], dOut:data[1]}
ot = optTF(optimizer, result, [W, cost], inpDict, 200, 50)
Here, I have checked the result, and see that it is what I want. So the optimiser is working fine. The model has been optimised. Now, I want to make a prediction with some other data. So I do ...
r = runTF(result, inpDict)
This new result is not what I would expect from the same trained model.
Now, as long as I stay within the same tf.Session(), we are ok. However, I do want to be able to do predictions even when I an done with a session. So my question is, how do I use a model once we have trained it in one session in a different session?
Note, the entire thing is something that I did in a different session?
Edited:
I edited the two functions to incorporate the saving ...
def runTF(func, inpDict, modelFile=None):
if modelFile is not None:
saver = tf.train.Saver()
sess = tf.Session()
init = tf.global_variables_initializer()
sess.run(init)
if modelFile is not None:
ckpt = tf.train.get_checkpoint_state(modelFile)
print(modelFile, ckpt)
if ckpt and ckpt.model_checkpoint_path:
saver.restore(sess, ckpt.model_checkpoint_path)
print('Session restored')
result = sess.run(func, feed_dict = inpDict)
sess.close()
return result
and
def optTF(opt, res, others, inpDict, nSteps, printSteps=50, modelFile='models/temp.ckpt'):
os, re = [], []
saver = tf.train.Saver()
sess = tf.Session()
init = tf.global_variables_initializer()
sess.run(init)
for i in range(nSteps):
# First run the optimizer ...
sess.run(opt, feed_dict = inpDict)
# Save all the data you want to save
temp = sess.run( [res] + others, feed_dict = inpDict)
re.append(temp[0])
os.append(temp[1:])
if (i%printSteps) == 0:
print('{:5d}'.format(i))
path = saver.save(sess, modelFile)
print('Model saved in: {}'.format(path))
sess.close()
return re, os
And running the model as:
ot = optTF(optimizer, result, [cost]+weights+biases, inpDict, 200, 50)
r = runTF([result], inpDict, 'models/temp.ckpt')
Still nothing! I checked that:
The value of ckpt is None
The models folder has the following files:
checkpoint
temp.ckpt.index
temp.ckpt.data-00000-of-00001
temp.ckpt.meta
You need to save and restore the session you are creating and training. As in
init = tf.initialize_all_variables()
saver = tf.train.Saver()
with tf.Session() as sess:
sess.run(init)
if restore:
ckpt = tf.train.get_checkpoint_state(save_path)
if ckpt and ckpt.model_checkpoint_path:
saver.restore(sess, ckpt.model_checkpoint_path)
else:
# ... training code omitted ...
saver.save(sess, save_path)
checkout also https://www.tensorflow.org/programmers_guide/saved_model if you want to create a full model with estimator instead of just one session.
I am trying to load a tensorflow model from disk and predicting the values.
Code
def get_value(row):
print("**********************************************")
graph = tf.Graph()
rowkey = row[0]
checkpoint_file = "/home/sahil/Desktop/Relation_Extraction/data/1485336002/checkpoints/model-300"
print("Loading model................................")
with graph.as_default():
session_conf = tf.ConfigProto(
allow_soft_placement=allow_soft_placement,
log_device_placement=log_device_placement)
sess = tf.Session(config=session_conf)
with sess.as_default():
# Load the saved meta graph and restore variables
saver = tf.train.import_meta_graph("{}.meta".format(checkpoint_file))
saver.restore(sess, checkpoint_file)
input_x = graph.get_operation_by_name("X_train").outputs[0]
dropout_keep_prob = graph.get_operation_by_name("dropout_keep_prob").outputs[0]
predictions = graph.get_operation_by_name("output/predictions").outputs[0]
batch_predictions = sess.run(predictions, {input_x: [row[1]], dropout_keep_prob: 1.0})
print(batch_predictions)
return (rowkey, batch_predictions)
I have a RDD which consists of a tuple (rowkey, input_vector). I want to use the loaded model to predict the score/class of the input.
Code to call get_value()
result = data_rdd.map(lambda iter: get_value(iter))
result.foreach(print)
The problem is every time I call the map, the model is loaded everytime for each tuple and it takes a lot of time.
I am thinking of loading the model using mapPartitions and then use map to call get_value function.
I have no clue as how to convert the code to a mapPartition where I load the tensorflow model only once per parition and reduce the running time.
Thanks in advance.
I am not sure if I get your question correctly, but we can optimise your code a bit here.
graph = tf.Graph()
checkpoint_file = "/home/sahil/Desktop/Relation_Extraction/data/1485336002/checkpoints/model-300"
with graph.as_default():
session_conf = tf.ConfigProto(
allow_soft_placement=allow_soft_placement,
log_device_placement=log_device_placement)
sess = tf.Session(config=session_conf)
s = sess.as_default()
saver = tf.train.import_meta_graph("{}.meta".format(checkpoint_file))
saver.restore(sess, checkpoint_file)
input_x = graph.get_operation_by_name("X_train").outputs[0]
dropout_keep_prob = graph.get_operation_by_name("dropout_keep_prob").outputs[0]
predictions = graph.get_operation_by_name("output/predictions").outputs[0]
session_pickle = cPickle.dumps(sess)
def get_value(key, vector, session_pickle):
sess = cPickle.loads(session_pickle)
rowkey = key
batch_predictions = sess.run(predictions, {input_x: [vector], dropout_keep_prob: 1.0})
print(batch_predictions)
return (rowkey, batch_predictions
result = data_rdd.map(lambda (key, row): get_value(key=key, vector = row , session_pickle = session_pickle))
result.foreach(print)
So you can serialize your tensorflow session. Though I haven't tested your code here. Run this and leave a comment.
I guess that the below code is a huge improvement as it uses mapPartitions.
Code
def predict(rows):
graph = tf.Graph()
checkpoint_file = "/home/sahil/Desktop/Relation_Extraction/data/1485336002/checkpoints/model-300"
print("Loading model................................")
with graph.as_default():
session_conf = tf.ConfigProto(
allow_soft_placement=allow_soft_placement,
log_device_placement=log_device_placement)
sess = tf.Session(config=session_conf)
with sess.as_default():
# Load the saved meta graph and restore variables
saver = tf.train.import_meta_graph("{}.meta".format(checkpoint_file))
saver.restore(sess, checkpoint_file)
print("**********************************************")
# Get the placeholders from the graph by name
input_x = graph.get_operation_by_name("X_train").outputs[0]
dropout_keep_prob = graph.get_operation_by_name("dropout_keep_prob").outputs[0]
# Tensors we want to evaluate
predictions = graph.get_operation_by_name("output/predictions").outputs[0]
# Generate batches for one epoch
for row in rows:
X_test = [row[1]]
batch_predictions = sess.run(predictions, {input_x: X_test, dropout_keep_prob:
yield (row[0], batch_predictions)
result = data_rdd.mapPartitions(lambda iter: predict(iter))
result.foreach(print)