Can I train a linear regression model with tf.data.Datasets? If I run the following code

import tensorflow as tf
import numpy as np

x = np.linspace(1, 10, num=10**2)
y = 54*x   33

ds = tf.data.Dataset.from_tensor_slices(list(zip(x, y)))

model = tf.keras.models.Sequential([
        tf.keras.layers.Dense(1, input_shape = [1,]),
        tf.keras.layers.Dense(10, activation="sigmoid"),
        tf.keras.layers.Dense(1)
    ])

model.compile(loss="mean_absolute_error", optimizer="adam")
model.fit(ds, epochs=5)

I get the error

ValueError: Target data is missing. Your model was compiled with loss=mean_absolute_error, and therefore expects target data to be provided in `fit()`.

It is possible to train like that?

CodePudding user response：

You need to consider:

1. Create dataset like from_tensor_slices((x,y))
2. Define and take dataset with batch, like : ds = ds.batch(32)

import tensorflow as tf
import numpy as np

x = np.linspace(1, 10, num=10**2)
y = 54*x   33

ds = tf.data.Dataset.from_tensor_slices((x,y))
ds = ds.batch(32)
model = tf.keras.models.Sequential([
        tf.keras.layers.Dense(1, input_shape = [1,]),
        tf.keras.layers.Dense(10, activation="sigmoid"),
        tf.keras.layers.Dense(1)
    ])

model.compile(loss="mean_absolute_error", optimizer="adam")
model.fit(ds, epochs=5)

Output:

Epoch 1/5
4/4 [==============================] - 0s 5ms/step - loss: 329.4714
Epoch 2/5
4/4 [==============================] - 0s 8ms/step - loss: 329.4355
Epoch 3/5
4/4 [==============================] - 0s 11ms/step - loss: 329.3994
Epoch 4/5
4/4 [==============================] - 0s 6ms/step - loss: 329.3628
Epoch 5/5
4/4 [==============================] - 0s 9ms/step - loss: 329.3259

Update: How to create a model and train for linear regression? You don't need a complex and large network only a Dense(1) with activation='linear' is OK.

import tensorflow as tf
import numpy as np

x = np.random.rand(10000)
y = 54*x   33

ds = tf.data.Dataset.from_tensor_slices((x,y))
ds = ds.batch(64)
model = tf.keras.models.Sequential([
        tf.keras.layers.Dense(1, input_shape = [1,]),
        tf.keras.layers.Dense(1, activation='linear')
    ])

model.compile(loss="mean_absolute_error", optimizer="adam")
model.fit(ds, epochs=50)

Epoch 1/50
157/157 [==============================] - 1s 2ms/step - loss: 60.0440
Epoch 2/50
157/157 [==============================] - 0s 2ms/step - loss: 59.6723
Epoch 3/50
157/157 [==============================] - 0s 2ms/step - loss: 59.1068
...
Epoch 48/50
157/157 [==============================] - 0s 2ms/step - loss: 0.1588
Epoch 49/50
157/157 [==============================] - 0s 2ms/step - loss: 0.0053
Epoch 50/50
157/157 [==============================] - 0s 3ms/step - loss: 0.0039