A toy example

import tensorflow as tf
from tensorflow import keras 
import numpy as np 
import matplotlib.pyplot as plt

Data

data = np.random.uniform(size=(64 * 1025, 1), low=-5., high=5.)

x_train = data[:64*1000, :]
x_test = data[64*1000:, :]

y_train = np.sin(x_train) #+ np.random.normal(size=x_train.shape) / 10.
y_test = np.sin(x_test)

plt.plot(x_train, y_train, '.')
plt.plot(x_test, y_test, '.')
plt.legend(["train", "test"])

<matplotlib.legend.Legend at 0x7fc66a0a6320>

Model

model = keras.Sequential(
    [
     keras.Input(1, name="input_layer"),
     keras.layers.Dense(32, activation=tf.nn.relu, name="hidden_layer_1"),
     keras.layers.Dense(32, activation=tf.nn.relu, name="hidden_layer_2"),
     #keras.layers.Dense(4, activation=tf.nn.relu, name="hidden_layer_3"),
     #keras.layers.Dense(4, activation=tf.nn.relu, name="hidden_layer_4"),
     keras.layers.Dense(1, name="output_layer")
    ]
)

model.compile(
    optimizer=keras.optimizers.Adam(learning_rate=0.001),
    loss=keras.losses.MeanSquaredError(),
    metrics=[keras.metrics.RootMeanSquaredError()]
)

Training

logdir = "log/toy_example"
tensorboard_callback = keras.callbacks.TensorBoard(logdir)

model.fit(
    x_train,
    y_train,
    batch_size=64,
    epochs=10,
    validation_data=(x_test, y_test),
    callbacks=[tensorboard_callback]
)

Epoch 1/10
1000/1000 [==============================] - 1s 1ms/step - loss: 1.0507 - root_mean_squared_error: 1.0107 - val_loss: 0.4452 - val_root_mean_squared_error: 0.6672
Epoch 2/10
1000/1000 [==============================] - 1s 996us/step - loss: 0.4354 - root_mean_squared_error: 0.6599 - val_loss: 0.4211 - val_root_mean_squared_error: 0.6489
Epoch 3/10
1000/1000 [==============================] - 1s 1ms/step - loss: 0.4142 - root_mean_squared_error: 0.6436 - val_loss: 0.4068 - val_root_mean_squared_error: 0.6378
Epoch 4/10
1000/1000 [==============================] - 1s 1ms/step - loss: 0.4039 - root_mean_squared_error: 0.6355 - val_loss: 0.4029 - val_root_mean_squared_error: 0.6348
Epoch 5/10
1000/1000 [==============================] - 1s 1ms/step - loss: 0.3972 - root_mean_squared_error: 0.6302 - val_loss: 0.4025 - val_root_mean_squared_error: 0.6344
Epoch 6/10
1000/1000 [==============================] - 1s 1ms/step - loss: 0.3979 - root_mean_squared_error: 0.6308 - val_loss: 0.4026 - val_root_mean_squared_error: 0.6345
Epoch 7/10
1000/1000 [==============================] - 1s 1ms/step - loss: 0.3982 - root_mean_squared_error: 0.6310 - val_loss: 0.4028 - val_root_mean_squared_error: 0.6347
Epoch 8/10
1000/1000 [==============================] - 1s 1ms/step - loss: 0.3997 - root_mean_squared_error: 0.6322 - val_loss: 0.4025 - val_root_mean_squared_error: 0.6344
Epoch 9/10
1000/1000 [==============================] - 1s 1ms/step - loss: 0.3994 - root_mean_squared_error: 0.6320 - val_loss: 0.4025 - val_root_mean_squared_error: 0.6344
Epoch 10/10
1000/1000 [==============================] - 1s 1ms/step - loss: 0.4017 - root_mean_squared_error: 0.6338 - val_loss: 0.4025 - val_root_mean_squared_error: 0.6344

<tensorflow.python.keras.callbacks.History at 0x7fc669dfd438>

x = np.linspace(-10., 10., 100)

plt.plot(x, np.sin(x))

print(x.shape)

x = np.reshape(x, (-1, 1))

print(x.shape)

plt.plot(x, model(x))

plt.legend(["sin", "model"])

(100,)
(100, 1)

<matplotlib.legend.Legend at 0x7fc669d30ba8>

model.summary()

keras.utils.plot_model(model, "model.png", show_shapes=True)

Model: "sequential_20"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
hidden_layer_1 (Dense)       (None, 3)                 6         
_________________________________________________________________
hidden_layer_2 (Dense)       (None, 3)                 12        
_________________________________________________________________
output_layer (Dense)         (None, 1)                 4         
=================================================================
Total params: 22
Trainable params: 22
Non-trainable params: 0
_________________________________________________________________

# %load_ext tensorboard
# %tensorboard --logdir log

model.save("model/toy_model")
model2 = keras.models.load_model("model/toy_model")

INFO:tensorflow:Assets written to: model/toy_model/assets