I coded some of the neural networks like image classifier,mnist and NLP I
got an accuracy of 98 percent on my GPU (NVIDIA GT 610). How can I feed new data (not training data) to my neural network and get the predictions?
Let's suppose:
Inputs Output
0 0 1 0
1 1 1 1
1 0 1 1
0 1 1 0
I got an accuracy of 98.7 how to give an input like [ 1 1 0] and predict the output. Is there any method in Tensorflow to do this?
If your output variable is y and your input placeholder is x:
sess.run(y, feed_dict={x: mnist.test.images})
See https://www.tensorflow.org/get_started/mnist/beginners
Related
My tensor training data set structure is as follows:
[[1 3 0 99 4 ... 9 0 9],
...
[3 2 3 1 3 ... 9 9 8]]
In which there are 798 "rows" or individual arrays and each array is of length 9000, or has 9000 "columns".
The label data is like this: [ [1 0 1 0 0 ... 0 0 0 1]]. It is one array with 798 columns.
model = tf.keras.models.Sequential()
model.add(tf.keras.layers.LSTM(100,batch_input_shape=(798,1000,9000)))
model.compile(loss='mean_absolute_error',optimizer='adam',metrics=['accuracy'])
model.fit(training_data,training_labels,batch_size=150,epochs=10,validation_data=(validation_data,validation_labels))
But I kept getting the error:
ValueError: Input 0 of layer sequential_2 is incompatible with the layer: expected ndim=3, found ndim=2. Full shape received: [None, 9000]
I'm not sure what I'm doing wrong. Should I specify the batch_input_shape? Should I specify the input_shape? What do samples, batch size, time step, and features mean in relation to my data?
Is it possible to calculate the internal node predictions of an xgboost model? The R package, gbm, provides a prediction for internal nodes of each tree.
The xgboost output, however only shows predictions for the final leaves of the model.
xgboost output:
Notice that the Quality column has the final prediction for the leaf node in row 6. I would like that value for each of the internal nodes as well.
Tree Node ID Feature Split Yes No Missing Quality Cover
1: 0 0 0-0 Sex=female 0.50000 0-1 0-2 0-1 246.6042790 222.75
2: 0 1 0-1 Age 13.00000 0-3 0-4 0-4 22.3424225 144.25
3: 0 2 0-2 Pclass=3 0.50000 0-5 0-6 0-5 60.1275253 78.50
4: 0 3 0-3 SibSp 2.50000 0-7 0-8 0-7 23.6302433 9.25
5: 0 4 0-4 Fare 26.26875 0-9 0-10 0-9 21.4425507 135.00
6: 0 5 0-5 Leaf NA <NA> <NA> <NA> 0.1747126 42.50
R gbm output:
In the R gbm package output, the prediction column contains values for both leaf nodes (SplitVar == -1) and the internal nodes. I would like access to these values from the xgboost model
SplitVar SplitCodePred LeftNode RightNode MissingNode ErrorReduction Weight Prediction
0 1 0.000000000 1 8 15 32.564591 445 0.001132514
1 2 9.500000000 2 3 7 3.844470 282 -0.085827382
2 -1 0.119585850 -1 -1 -1 0.000000 15 0.119585850
3 0 1.000000000 4 5 6 3.047926 207 -0.092846157
4 -1 -0.118731665 -1 -1 -1 0.000000 165 -0.118731665
5 -1 0.008846912 -1 -1 -1 0.000000 42 0.008846912
6 -1 -0.092846157 -1 -1 -1 0.000000 207 -0.092846157
Question:
How do I access or calculate predictions for the internal nodes of an xgboost model? I would like to use them for a greedy, poor man's version of SHAP scores.
The solution to this problem is to dump the xgboost json object with all_stats=True. That adds the cover statistic to the output which can be used to distribute the leaf points through the internal nodes:
def _calculate_contribution(node: AnyNode) -> float32:
if isinstance(node, Leaf):
return node.contrib
else:
return (
node.left.cover * Node._calculate_contribution(node.left)
+ node.right.cover * Node._calculate_contribution(node.right)
) / node.cover
The internal contribution is the weighted average of the child contributions. Using this method, the generated results exactly match those returned when calling the predict method with pred_contribs=True and approx_contribs=True.
Problem: How can i convert a .tflite (serialised flat buffer) to .pb (frozen model)? The documentation only talks about one way conversion.
Use-case is: I have a model that is trained on converted to .tflite but unfortunately, i do not have details of the model and i would like to inspect the graph, how can i do that?
I found the answer here
We can use Interpreter to analysis the model and the same code looks like following:
import numpy as np
import tensorflow as tf
# Load TFLite model and allocate tensors.
interpreter = tf.lite.Interpreter(model_path="converted_model.tflite")
interpreter.allocate_tensors()
# Get input and output tensors.
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()
# Test model on random input data.
input_shape = input_details[0]['shape']
input_data = np.array(np.random.random_sample(input_shape), dtype=np.float32)
interpreter.set_tensor(input_details[0]['index'], input_data)
interpreter.invoke()
output_data = interpreter.get_tensor(output_details[0]['index'])
print(output_data)
Netron is the best analysis/visualising tool i found, it can understand lot of formats including .tflite.
I don't think there is a way to restore tflite back to pb as some information are lost after conversion. I found an indirect way to have a glimpse on what is inside tflite model is to read back each of the tensor.
interpreter = tf.contrib.lite.Interpreter(model_path=model_path)
interpreter.allocate_tensors()
# trial some arbitrary numbers to find out the num of tensors
num_layer = 89
for i in range(num_layer):
detail = interpreter._get_tensor_details(i)
print(i, detail['name'], detail['shape'])
and you would see something like below. As there are only limited of operations that are currently supported, it is not too difficult to reverse engineer the network architecture. I have put some tutorials too on my Github
0 MobilenetV1/Logits/AvgPool_1a/AvgPool [ 1 1 1 1024]
1 MobilenetV1/Logits/Conv2d_1c_1x1/BiasAdd [ 1 1 1 1001]
2 MobilenetV1/Logits/Conv2d_1c_1x1/Conv2D_bias [1001]
3 MobilenetV1/Logits/Conv2d_1c_1x1/weights_quant/FakeQuantWithMinMaxVars [1001 1 1 1024]
4 MobilenetV1/Logits/SpatialSqueeze [ 1 1001]
5 MobilenetV1/Logits/SpatialSqueeze_shape [2]
6 MobilenetV1/MobilenetV1/Conv2d_0/Conv2D_Fold_bias [32]
7 MobilenetV1/MobilenetV1/Conv2d_0/Relu6 [ 1 112 112 32]
8 MobilenetV1/MobilenetV1/Conv2d_0/weights_quant/FakeQuantWithMinMaxVars [32 3 3 3]
9 MobilenetV1/MobilenetV1/Conv2d_10_depthwise/Relu6 [ 1 14 14 512]
10 MobilenetV1/MobilenetV1/Conv2d_10_depthwise/depthwise_Fold_bias [512]
11 MobilenetV1/MobilenetV1/Conv2d_10_depthwise/weights_quant/FakeQuantWithMinMaxVars [ 1 3 3 512]
12 MobilenetV1/MobilenetV1/Conv2d_10_pointwise/Conv2D_Fold_bias [512]
13 MobilenetV1/MobilenetV1/Conv2d_10_pointwise/Relu6 [ 1 14 14 512]
14 MobilenetV1/MobilenetV1/Conv2d_10_pointwise/weights_quant/FakeQuantWithMinMaxVars [512 1 1 512]
15 MobilenetV1/MobilenetV1/Conv2d_11_depthwise/Relu6 [ 1 14 14 512]
16 MobilenetV1/MobilenetV1/Conv2d_11_depthwise/depthwise_Fold_bias [512]
17 MobilenetV1/MobilenetV1/Conv2d_11_depthwise/weights_quant/FakeQuantWithMinMaxVars [ 1 3 3 512]
18 MobilenetV1/MobilenetV1/Conv2d_11_pointwise/Conv2D_Fold_bias [512]
19 MobilenetV1/MobilenetV1/Conv2d_11_pointwise/Relu6 [ 1 14 14 512]
20 MobilenetV1/MobilenetV1/Conv2d_11_pointwise/weights_quant/FakeQuantWithMinMaxVars [512 1 1 512]
I have done this with TOCO, using tf 1.12
tensorflow_1.12/tensorflow/bazel-bin/tensorflow/contrib/lite/toco/toco --
output_file=coco_ssd_mobilenet_v1_1.0_quant_2018_06_29.pb --
output_format=TENSORFLOW_GRAPHDEF --input_format=TFLITE --
input_file=coco_ssd_mobilenet_v1_1.0_quant_2018_06_29.tflite --
inference_type=FLOAT --input_type=FLOAT --input_array="" --output_array="" --
input_shape=1,450,450,3 --dump_grapHviz=./
(you can remove the dump_graphviz option)
I am using Keras with Tensorflow backend.
I am facing a batch size limitation due to high memory usage
My data is composed of 4 1D signals treated with a sample size of 801 for each channel. Global sample size is 3204
Input data:
4 channels of N 1D signals of length 7003
Input generated by applying a sliding window on 1D signals
Give input data shape (N*6203, 801, 4)
N is the number of signals used to build one batch
My Model:
Input 801 x 4
Conv2D 5 x 1, 20 channels
MaxPooling 2 x 1
Conv2D 5 x 1, 20 channels
MaxPooling 2 x 1
Conv2D 5 x 1, 20 channels
MaxPooling 2 x 1
Conv2D 5 x 1, 20 channels
Flatten
Dense 2000
Dense 5
With my GPU (Quadro K6000, 12189 MiB) i can fit only N=2 without warning
With N=3 I get a ran out of memory warning
With N=4 I get a ran out of memory error
It sound like batch_size is limitated by the space used by all tensors.
Input 801 x 4 x 1
Conv 797 x 4 x 20
MaxPooling 398 x 4 x 20
Conv 394 x 4 x 20
MaxPooling 197 x 4 x 20
Conv 193 x 4 x 20
MaxPooling 96 x 4 x 20
Conv 92 x 4 x 20
Dense 2000
Dense 5
With a 1D signal of 7001 with 4 channels -> 6201 samples
Total = N*4224 MiB.
N=2 -> 8448 MiB fit in GPU
N=3 -> 12672 MiB work but warning: failed to allocate 1.10 GiB then 3.00 GiB
N=4 -> 16896 MiB fail, only one message: failed to allocate 5.89 GiB
Does it work like that ? Is there any way to reduce the memory usage ?
To give a time estimation: 34 batch run in 40s and I got N total = 10^6
Thank you for your help :)
Example with python2.7: https://drive.google.com/open?id=1N7K_bxblC97FejozL4g7J_rl6-b9ScCn
I have a byte tensor of integer class labels, e.g. from the MNIST data set.
1
7
5
[torch.ByteTensor of size 3]
How do use it to create a tensor of 1-hot vectors?
1 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 1 0 0 0
0 0 0 0 1 0 0 0 0 0
[torch.DoubleTensor of size 3x10]
I know I could do this with a loop, but I'm wondering if there's any clever Torch indexing that will get it for me in a single line.
indices = torch.LongTensor{1,7,5}:view(-1,1)
one_hot = torch.zeros(3, 10)
one_hot:scatter(2, indices, 1)
You can find the documentation for scatter in the torch/torch7 github readme (in the master branch).
An alternate method is to shuffle rows from an identity matrix:
indicies = torch.LongTensor{1,7,5}
one_hot = torch.eye(10):index(1, indicies)
This was not my idea, I found it in karpathy/char-rnn.