Search…
⌃K

Model Integration

In this section, we will set up our classification model by either importing or building it.

Project Setup

UI
CLI
First step is to Create a Project with the nameIMDB, and the description Review Classifier.
The project name, IMDB, is set with the leap init command, as discussed in Dataset Integration above.
The project page contains the Versions, 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
Building a Model
CLI

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:
imdb-dense.h5
10MB
Binary

Import Model

  1. 1.
    In the Network view, click the
    Import Model button to open the Import Model panel.
  2. 2.
    Set the Revision Name to imdb-dense and the Model Name to pre-trained.
  3. 3.
    For File Type, use H5_TF2 and select imdb-dense.h5 in the Upload File field.
  4. 4.
    Click the Import button.
  5. 5.
    Once completed, the imported model, imdb-dense, is added to the Versions view. Position your cursor over that version, click
    to Open Commit.
  6. 6.
    Back in the Network view, set the Dataset Block to point to the imdb Dataset Instance, and connect it to the first layer.
​
Importing a Model and Dataset Block Setup

Building our Classification Model using UI

In this section, we will add layers, update their properties, and connect them together to form the model.
The model that we will build is based on a small dense neural network described below:
Layers
Properties
Embedding
input_dim=10001, output_dim=250
Dropout
rate=0.2
Dense
units=28, activation="relu"'
GlobalAveragePooling1D
​
Dropout
rate=0.2
Dense
units=2, activation="softmax"

Adding Layers and Their Properties

In the Network view, follow these steps for each layer:
  1. 1.
    Right-click and add the corresponding layer. More info at Add Layers.
  2. 2.
    Update the corresponding properties in the Layer Properties view on the right. More info at Layer Properties.
  3. 3.
    Connect the Dataset to the first layer, and then connect all the layers in order. More info at Connections.
All steps are illustrated below.
Add Layers and Connect Them

Build Model

Copy the imdb_dense.py file to the imdb folder we created in Dataset Integration. This file defines the dense model, and can be found here:
imdb_dense.py
580B
Text

Tensorleap Model Integration Script

Edit the .tensorleap/model.py file and point it to the model defined in imdb_dense.py:
from pathlib import Path
from imdb_dense import build_model
​
​
def leap_save_model(target_file_path: Path):
# Load your model
model = build_model()
# Save it to the path supplied as an arugment (has a .h5 suffix)
model.save(target_file_path)
​
To validate the correctness of our code, run the following command:
leap check --all
If done correctly, there should be no errors and we can move forward and push the model using the following command:
leap push --model --description=imdb-dense --model-name=pre-trained

Opening the Model in the UI

Once completed, open the UI, where you should see the imported model, imdb_dense,in the Versions view.
  • Position your cursor over the view and click
    on the right to Open Commit.
  • Back on 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:
Connected Layers and Dataset
Each block shows the calculated output shape affected by the preceding layers.

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:
  1. 1.
    Right-click and add the following:
    • Loss -> CategoricalCrossentropy
    • GroundTruth, and set it to Ground Truth - classes
    • Optimizer -> Adam
  2. 2.
    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 Loss and Optimizer

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:
  1. 1.
    Right-click and choose Visualizer to add it.
  2. 2.
    Click the Visualizer node to open up the Visualizer Details on the right.
  3. 3.
    Choose text_from_token from the Selected Visulizer list.
  4. 4.
    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:
  1. 1.
    Right-click and choose Visualizer to add it.
  2. 2.
    Click the Visualizer node to open up the Visualizer Details on the right.
  3. 3.
    Choose HorizontalBar from the Selected Visualizer list.
  4. 4.
    Connect the last Dense layer output to the input of the Visualizer node.
  5. 5.
    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.
​
Full Model with Dataset, Layers, Loss and an Optimizer
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.
The newly-saved version appears on the Versions view

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.
Train Model Dialog

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.
Add a Dashboard and a Loss Dashlet
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.
Loss vs Batch
Accuracy vs Batch - Reaching 89% Accuracy

Up 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.
Previous
Dataset Integration
Next
Model Perception Analysis
Last modified 8mo ago