I am new to tensorflow-2 and I was starting my learning curve, with the follow simple Linear-Regression model:
import tensorflow as tf import numpy as np import matplotlib.pyplot as plt # Make data num_samples, w, b = 20, 0.5, 2 xs = np.asarray(range(num_samples)) ys = np.asarray([x*w + b + np.random.normal() for x in range(num_samples)]) xts = tf.convert_to_tensor(xs, dtype=tf.float32) yts = tf.convert_to_tensor(xs, dtype=tf.float32) plt.plot(xs, ys, 'ro') class Linear(tf.keras.Model): def __init__(self, name='linear', **kwargs): super().__init__(name='linear', **kwargs) self.w = tf.Variable(0, True, name="w", dtype=tf.float32) self.b = tf.Variable(1, True, name="b", dtype=tf.float32) def call(self, inputs): return self.w*inputs + self.b class Custom(tf.keras.callbacks.Callback): def on_epoch_end(self, epoch, logs=None): if epoch % 20 == 0: preds = self.model.predict(xts) plt.plot(xs, preds, label='{} {:7.2f}'.format(epoch, logs['loss'])) print('The average loss for epoch {} is .'.format(epoch, logs['loss'])) x = tf.keras.Input(dtype=tf.float32, shape=[]) #model = tf.keras.Sequential([tf.keras.layers.Dense(units=1, input_shape=[1])]) model = Linear() optimizer = tf.keras.optimizers.RMSprop(learning_rate=0.001) model.compile(optimizer=optimizer, loss='MSE') model.fit(x=xts, y=yts, verbose=1, batch_size=4, epochs=250, callbacks=[Custom()]) plt.legend() plt.show() For a reason I don't understand it seems like my model is not fitting the curve. I also tried with keras.layers.Dense(1) and I had the same exact result. Also it seems like the results don't correspond to a proper loss function, as around epoch 120 the model should have less loss than on 250.
Can you maybe help me understand what I am doing wrong? Thanks a lot!
