TensorFlow undefined options value

Issue

When I tried to use model.fit in Tensorflow and add options it keeps showing me this error for batchSize and epochs, I have checked the docs and it is the same as I did so what could be the error

model = Sequential([
    Dense(units=16,input_shape=(1,), activation='relu'), # layer 1
    Dense(units=32, activation='relu'), # layer 2
    Dense(units=2, activation='softmax'), # layer 3 fully connected layer
])

enter image description here

model.compile(optimizer=Adam(learning_rate=0.0001), loss='sparse_categorical_crossentropy', metrics=['aaccracy'])

model.fit(scaled_train_samples, train_labels, {
   batchSize: 4,
   epochs: 3
})

enter image description here

I tried to use it this way and it show me this error also

model.fit(scaled_train_samples, train_labels, batch_size=4,epochs=3)

enter image description here

scaled_train_samples

[
[0.22093023]
[0.6627907]
[0.44186047]
[0.40697674]
[0.97674419]
[0.04651163]
[0.19767442]
[0.61627907]
[0.03488372]
[0.44186047]
[0.43023256]
[0.8255814]
[0.48837209]
[0.20930233]
[0.46511628]
[0.81395349]
[0.15116279]
[0.18604651]
[0.43023256]
[0.61627907]
]

train_labels

[
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0

]

Solution

Try this:

import tensorflow as tf
import numpy as np

scaled_train_samples = [
    [0.44186047],[0.40697674],[0.97674419],[0.04651163],[0.19767442],
    [0.61627907],[0.03488372],[0.44186047],[0.43023256],[0.8255814],
    [0.48837209],[0.20930233],[0.46511628],[0.81395349],[0.15116279],
    [0.18604651],[0.43023256],[0.61627907]]

train_labels = [1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0]

print(np.asarray(scaled_train_samples).shape)
# (18, 1)

print(np.asarray(train_labels).shape)
# (18,)

model = tf.keras.Sequential([
    tf.keras.layers.Dense(units=16,input_shape=(1,), activation='relu'), # layer 1
    tf.keras.layers.Dense(units=32, activation='relu'), # layer 2
    tf.keras.layers.Dense(units=2, activation='softmax'), # layer 3 fully connected layer
])

model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.0001), 
              loss='sparse_categorical_crossentropy', 
              metrics=['acc'])

model.fit(scaled_train_samples, train_labels, batch_size=4, epochs=3)

Epoch 1/3
5/5 [==============================] - 0s 3ms/step - loss: 0.6921 - acc: 0.5000
Epoch 2/3
5/5 [==============================] - 0s 2ms/step - loss: 0.6916 - acc: 0.5000
Epoch 3/3
5/5 [==============================] - 0s 7ms/step - loss: 0.6914 - acc: 0.5556

Answered By – I'mahdi

This Answer collected from stackoverflow, is licensed under cc by-sa 2.5 , cc by-sa 3.0 and cc by-sa 4.0

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