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Visualization

Summary​

Visualization of

  • Data
  • Model
  • Training of a model
  • Predictions of a model

Content​

Visualizing Data​

import matplotlib.pyplot as plt

X = tf.range(-100, 100, 3)
y = X + 10

len70p = int(len(X) / 100 * 80)

X_train = X[:len70p]
y_train = y[:len70p]

X_test = X[len70p:]
y_test = y[len70p:]

plt.scatter(X_train, y_train, c="b", label="training data")
plt.scatter(X_test, y_test, c="r", label="testing data")
plt.legend()

![[Pasted image 20231020220634.png]]

Visualizing Model​

Method !::

model = tf.keras.Sequential([
    tf.keras.layers.Input(1),
    tf.keras.layers.Dense(100),
    tf.keras.layers.Dense(100),
    tf.keras.layers.Dense(1)
])

model.summary()

"""
Model: "sequential"
_________________________________________________________________
 Layer (type)                Output Shape              Param #
=================================================================
 dense (Dense)               (None, 100)               200

 dense_1 (Dense)             (None, 100)               10100

 dense_2 (Dense)             (None, 1)                 101

=================================================================
Total params: 10,401
Trainable params: 10,401
Non-trainable params: 0
_________________________________________________________________
"""

Method 2:

model = tf.keras.Sequential([
    tf.keras.layers.Input(1),
    tf.keras.layers.Dense(100),
    tf.keras.layers.Dense(100),
    tf.keras.layers.Dense(1)
])

tf.keras.utils.plot_model(
    model=model,
    show_shapes=True,
    show_dtype=True,
    show_trainable=True
)

![[Pasted image 20231020221539.png]]

Visualizing the training of a model​

Visualizing the predictions of a model​

y_predict = model.predict(X_test)

import matplotlib.pyplot as plt

plt.scatter(X_test, y_test, c="g", label="Expected")
plt.scatter(X_test, y_predict, c="red", label="Actual")

![[Pasted image 20231024182807.png]]