Model Integration
In this section, we will set up our classification model by either importing or building it.
Project Setup
First step is to Create a Project with the nameIMDB
, and the description Review Classifier
.
The project page contains the Network and Dashboard views. To toggle them, click the buttons at the top.
Dataset Block Setup
We'll start by pointing the model's dataset block to our imdb
dataset. This will update the Dataset Block with the relevant input.
In the Network view, click the Dataset Block to display the Dataset Details panel on the right, then click Connect Dataset and select imdb
dataset from the list.
A more detailed explanation about this step can be found at Dataset Block.
Set Model Layers
Importing a Model from Tensorflow or PyTorch
Tensorleap can import a model saved by Tensorflow (PB/H5/JSON_TF2) and PyTorch (ONNX). More information can be found at Import Model.
In this sample code, a simple dense model is created using Tensorflow and then saved as a file named imdb-dense.h5
.
import tensorflow as tf
MAX_FEATURES = 10000
SEQUENCE_LENGTH = 250
vectorized_inputs = tf.keras.Input(shape=250, dtype="int64")
x = tf.keras.layers.Embedding(MAX_FEATURES + 1, SEQUENCE_LENGTH)(vectorized_inputs)
x = tf.keras.layers.Dropout(0.2)(x)
x = tf.keras.layers.Dense(28, activation='relu')(x)
x = tf.keras.layers.GlobalAveragePooling1D()(x)
x = tf.keras.layers.Dropout(0.2)(x)
output = tf.keras.layers.Dense(2, activation='softmax')(x)
model = tf.keras.Model(inputs=vectorized_inputs, outputs=output)
model.save('imdb-dense.h5')
For your convenience, you can also download this file here:
Import Model
In the Network view, click the
Import Model
button to open theImport Model
panel.Set the Revision Name to
imdb-dense
and the Model Name topre-trained
.For File Type, use
H5_TF2
and selectimdb-dense.h5
in the Upload File field.Click the Import button.
Once completed, the imported model,
imdb-dense,
is added to the Versions view. Position your cursor over that version, clickto Open Commit.
Back in the Network view, set the Dataset Block to point to the
imdb
Dataset Instance, and connect it to the first layer.

Once all layers have been connected to each other, the model should look like this:

Add Loss and Optimizer
In this section, we will set the Categorical Crossentropy loss function, and connect it to both the dataset's ground truth and the last layer in our model. We'll then add an Adam Optimizer block and connect it to the loss block. For more information at Loss and Optimizer.
After completing this section, our model will be ready for training.
Within the Network view:
Right-click and add the following:
Loss -> CategoricalCrossentropy
GroundTruth, and set it to
Ground Truth - classes
Optimizer -> Adam
Connect the last Dense layer and the GroundTruth to the CategoricalCrossentropy block. Additionally, connect the Loss block to the Adam optimizer.
All steps are illustrated below.

Add Visualizers
Visualizers defines how to visualize tensors within the model graph. For more info, see Visualizers.
Dataset Input Visualizer
There must be at least one visualizer connected to the model's input for analysis. To add the Visualizer for the input, follow the steps below.
Within the Network view:
Right-click and choose Visualizer to add it.
Click the Visualizer node to open up the Visualizer Details on the right.
Choose
text_from_token
from theSelected Visulizer
list.Connect the Dataset node output to the input of the Visualizer node.
Prediction and Ground Truth Visualizers
Additional visualizers will be connected to the prediction and ground truth.
To visualize the model's prediction output, follow the steps below:
Within the Network view:
Right-click and choose Visualizer to add it.
Click the Visualizer node to open up the Visualizer Details on the right.
Choose
HorizontalBar
from theSelected Visualizer
list.Connect the last Dense layer output to the input of the Visualizer node.
Repeat the steps, and connect the second visualizer to the GroundTruth node's output.
Save Network Version
Great! Your first version is ready to be saved.

Click the button and set the
Revision Name
to dense-nn
(Dense Neural Network). This adds the new version to the Versions view. For more information, see Save a Version.

Training
Tensorleap can import trained and untrained models. In our case, the model was created from scratch and needs to be trained.
To train the model, click from the top bar. Let's set Batch Size to
32
and the Number of Epochs to 5
and click . For more information, see Evaluate/Train Model.

Metrics
Once training begins, you can start tracking metrics in real-time.
Add a Dashboard and Dashlets
To add a new Metrics Dashboard, click and fill in the dashboard name -
imdb
.
Make sure that the model is selected from the Versions view, and add a new Dashlet.
Click for a Line Dashlet, set the name to
Loss
, and turn on Split series by subset
to separate training
and validation
metrics.

Next, add another Line Dashlet for the accuracy. Follow all the previous steps to add it and set the Dashlet Name to Accuracy
and set the Y-Axis to metrics.Accuracy
. Do not forget turn on Split series by subset
.
Once training begins, you can start tracking metrics in real-time.
From the Dashboard view on the right, select the tl_default_metrics
from the list at the top. This will open up the Metrics dashboard.
As training progresses, you should see loss values declining and accuracy values increasing. When training is completed, the model achieved an accuracy of 89% at the 3rd epoch.


89%
AccuracyUp Next - Model Perception Analysis
Tensorleap provides a host of innovative tools for model analysis and debugging. It tracks how each sample flows through each layer, and how each learned feature flows through the model. It also stores metrics in a big dataset to analyze the model's response to data.
We've discussed integrating and training our model. It's now time to analyze it. The next part of this tutorial will demonstrate various analyses of the model and data.
Once you are ready, proceed to Model Perception Analysis.
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