I have for example X_train (70,513) X_test (70,2) Y_train(30,513) Y_train(30,2) how can I get the tensor-flow model that I trained to output (0,1) array values for the test values.
Since you've not provided your model I'll assume you've done it the standard way.
I'm assuming after designing your model your output is stored in the tensor (say) prediction.
To see the prediction you need to create a session and feed in the required placeholders. This is how you do it:
sess = tf.Session()
sess.run(prediction, feed_dict={features: [[0.4, 1.1]]})
Since I don't know how your placeholders are defined, I made features a placeholder and feeded it a matrix of data.
Make sure you've initialized your variables using
sess = tf.Session()
sess.run(tf.global_variable_initializer())
Related
I am implementing an NLP model based on BERT, using tf.TPUEstimator(). I want to implement layer-wise training, where I need to select only one layer of the model to train for each epoch. In order to do this I wanted to change my model_fn and get the value of current_epoch.
I know how to compute the value of current_epoch as a tensor using tf.train.get_or_create_global_step() inside the model_fn BUT, I need to evaluate the value of this tensor to select which layer to train and implement return the correct train_op to the tf.estimator (train_op pertaining to a single layer chosen accrding to the value of the current_epoch).
I am unable to evaluate this tensor (current_epoch / global_step) from inside the model_fn. I tried the following but the training hangs at the step my_sess.run(my_global_step.initializer
global_step = tf.train.get_or_create_global_step()
graph = tf.get_default_graph()
my_sess = tf.Session(graph=graph)
current_epoch = (global_step * full_bs) // train_size
my_sess.run(my_global_step.initializer)
current_epoch = sess.run(current_epoch)
# My program hangs at the initialising step: my_sess.run(my_global_step.initializer)
Is there any way to evaluate a tensor using the tf.Estimators default session? How do I get the default session/ Graph?
Most importantly what is wrong in my code and why does the training hang when using tpu's and TPUEstimator?
This is not direct answer to OP's 2nd question, it is answer to the title.
I managed to print variable value with get_variable_value, but not sure if this is optimal way.
with
estimator = tf.contrib.tpu.TPUEstimator(
# ...
)
out = estimator.get_variable_value('output_bias')
print(type(out))
print(out)
I got
<class 'numpy.ndarray'>
[-0.00107745 0.00107744]
Everyone!
I have a question releate in trained model reusing( tensorflow ).
I have train model
I want predict new data used trained model.
I use DNNClassifier.
I have a model.ckpt-200000.meta, model.ckpt-200000.index, checkpoint, and eval folder.
but I don't know reuse this model..
plz help me.
First, you need to import your graph,
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
new_saver = tf.train.import_meta_graph('model.ckpt-200000.meta')
new_saver.restore(sess, tf.train.latest_checkpoint('./'))
Then you can give input to the graph and get the output.
graph = tf.get_default_graph()
input = graph.get_tensor_by_name("input:0")#input tensor by name
feed_dict ={input:} #input to the model
#Now, access theoutput operation.
op_to_restore = graph.get_tensor_by_name("y_:0") #output tensor
print sess.run(op_to_restore,feed_dict) #get output here
Few things to note,
You can replace the above code with your training part of the graph
(i.e you can get the output without training).
However, you still have to construct your graph as previously and
only replace the training part.
Above method only loading the weights for the constructed graph. Therefore, you have to construct the graph first.
A good tutorial on this can be found here, http://cv-tricks.com/tensorflow-tutorial/save-restore-tensorflow-models-quick-complete-tutorial/
If you don't want to construct the graph again you can follow this tutorial, https://blog.metaflow.fr/tensorflow-how-to-freeze-a-model-and-serve-it-with-a-python-api-d4f3596b3adc
I wanna draw the weights of tf.layers.dense in tensorboard histogram, but it not show in the parameter, how could I do that?
The weights are added as a variable named kernel, so you could use
x = tf.dense(...)
weights = tf.get_default_graph().get_tensor_by_name(
os.path.split(x.name)[0] + '/kernel:0')
You can obviously replace tf.get_default_graph() by any other graph you are working in.
I came across this problem and just solved it. tf.layers.dense 's name is not necessary to be the same with the kernel's name's prefix. My tensor is "dense_2/xxx" but it's kernel is "dense_1/kernel:0". To ensure that tf.get_variable works, you'd better set the name=xxx in the tf.layers.dense function to make two names owning same prefix. It works as the demo below:
l=tf.layers.dense(input_tf_xxx,300,name='ip1')
with tf.variable_scope('ip1', reuse=True):
w = tf.get_variable('kernel')
By the way, my tf version is 1.3.
The latest tensorflow layers api creates all the variables using the tf.get_variable call. This ensures that if you wish to use the variable again, you can just use the tf.get_variable function and provide the name of the variable that you wish to obtain.
In the case of a tf.layers.dense, the variable is created as: layer_name/kernel. So, you can obtain the variable by saying:
with tf.variable_scope("layer_name", reuse=True):
weights = tf.get_variable("kernel") # do not specify
# the shape here or it will confuse tensorflow into creating a new one.
[Edit]: The new version of Tensorflow now has both Functional and Object-Oriented interfaces to the layers api. If you need the layers only for computational purposes, then using the functional api is a good choice. The function names start with small letters for instance -> tf.layers.dense(...). The Layer Objects can be created using capital first letters e.g. -> tf.layers.Dense(...). Once you have a handle to this layer object, you can use all of its functionality. For obtaining the weights, just use obj.trainable_weights this returns a list of all the trainable variables found in that layer's scope.
I am going crazy with tensorflow.
I run this:
sess.run(x.kernel)
after training, and I get the weights.
Comes from the properties described here.
I am saying that I am going crazy because it seems that there are a million slightly different ways to do something in tf, and that fragments the tutorials around.
Is there anything wrong with
model.get_weights()
After I create a model, compile it and run fit, this function returns a numpy array of the weights for me.
In TF 2 if you're inside a #tf.function (graph mode):
weights = optimizer.weights
If you're in eager mode (default in TF2 except in #tf.function decorated functions):
weights = optimizer.get_weights()
in TF2 weights will output a list in length 2
weights_out[0] = kernel weight
weights_out[1] = bias weight
the second layer weight (layer[0] is the input layer with no weights) in a model in size: 50 with input size: 784
inputs = keras.Input(shape=(784,), name="digits")
x = layers.Dense(50, activation="relu", name="dense_1")(inputs)
x = layers.Dense(50, activation="relu", name="dense_2")(x)
outputs = layers.Dense(10, activation="softmax", name="predictions")(x)
model = keras.Model(inputs=inputs, outputs=outputs)
model.compile(...)
model.fit(...)
kernel_weight = model.layers[1].weights[0]
bias_weight = model.layers[1].weights[1]
all_weight = model.layers[1].weights
print(len(all_weight)) # 2
print(kernel_weight.shape) # (784,50)
print(bias_weight.shape) # (50,)
Try to make a loop for getting the weight of each layer in your sequential network by printing the name of the layer first which you can get from:
model.summary()
Then u can get the weight of each layer running this code:
for layer in model.layers:
print(layer.name)
print(layer.get_weights())
This is a simple thing which I just couldn't figure out how to do.
I converted a pre-trained VGG caffe model to tensorflow using the github code from https://github.com/ethereon/caffe-tensorflow and saved it to vgg16.npy...
I then load the network to my sess default session as "net" using:
images = tf.placeholder(tf.float32, [1, 224, 224, 3])
net = VGGNet_xavier({'data': images, 'label' : 1})
with tf.Session() as sess:
net.load("vgg16.npy", sess)
After net.load, I get a graph with a list of tensors. I can access individual tensors per layer using net.layers['conv1_1']... to get weights and biases for the first VGG convolutional layer, etc.
Now suppose that I make another graph that has as its first layer "h_conv1_b":
W_conv1_b = weight_variable([3,3,3,64])
b_conv1_b = bias_variable([64])
h_conv1_b = tf.nn.relu(conv2d(im_batch, W_conv1_b) + b_conv1_b)
My question is -- how do you get to assign the pre-trained weights from net.layers['conv1_1'] to h_conv1_b ?? (both are now tensors)
I suggest you have a detailed look at network.py from the https://github.com/ethereon/caffe-tensorflow, especially the function load(). It would help you understand what happened when you called net.load(weight_path, session).
FYI, variables in Tensorflow can be assigned to a numpy array by using var.assign(np_array) which is executed in the session. Here is the solution to your question:
with tf.Session() as sess:
W_conv1_b = weight_variable([3,3,3,64])
sess.run(W_conv1_b.assign(net.layers['conv1_1'].weights))
b_conv1_b = bias_variable([64])
sess.run(b_conv1_b.assign(net.layers['conv1_1'].biases))
h_conv1_b = tf.nn.relu(conv2d(im_batch, W_conv1_b) + b_conv1_b)
I would like to kindly remind you the following points:
var.assign(data) where 'data' is a numpy array and 'var' is a TensorFlow variable should be executed in the same session where you want to continue to execute your network either inference or training.
The 'var' should be created as the same shape as the 'data' by default. Therefore, if you can obtain the 'data' before creating the 'var', I suggest you create the 'var' by the method var=tf.Variable(shape=data.shape). Otherwise, you need to create the 'var' by the method var=tf.Variable(validate_shape=False), which means the variable shape is feasible. Detailed explainations can be found in the Tensorflow's API doc.
I extend the same repo caffe-tensorflow to support theano in caffe so that I can load the transformed model from caffe in Theano. Therefore, I am a reasonable expert w.r.t this repo's code. Please feel free to get in contact with me as you have any further question.
You can get variable values using eval method of tf.Variable-s from the first network and load that values into variables of the second network using load method (also method of the tf.Variable).
I am trying to use a Tensorflow model in two separate functions: one that trains it, and one used to test it. For example, the training function looks something like this:
graph = tf.Graph()
with graph.as_default():
tf_dataset = tf.placeholder(tf.float32, shape=(None, num_dims))
...
weights = tf.Variable(tf.truncated_normal([num_dims, num_labels]))
...
optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss)
prediction = tf.nn.softmax(logits)
...
session = tf.Session(graph=graph)
...
The other, evaluation function would just use prediction with the test data, like so:
session.run(prediction, feed_dict={tf_dataset: test_data})
The problem is, of course, that tf_dataset is not in the scope of the other function. I am fine with returning session and prediction from the training function, but having to share every single placeholder with the evaluation code seems a bit lame.
Is there a way to get the references somehow, from the session or the graph? Also, are there any good practices on how to separate training and evaluation code in Tensorflow?
You could give your placeholders unique names and use that. IE,
tf_dataset = tf.placeholder(tf.float32, shape=(None, num_dims), name="datainput")
...
sess.run(..., feed_dict={"datainput:0": mydata})
You can also get names/type pairs for all ops in your graph, so you could recover all the placeholder tensor names that way
[(op.name+":0", op.op_def.name) for op in graph.get_operations()]