Using predict() method after the training process is completed in TensorFlow

I use a callback to stop the training after my loss goes below a certain value. Once the training is over, I call the predict() method on the training input, however, when I calculate the loss function manually, I get a quiet bad result. Is using predict() wrong? or am I doing something else wrong?

import numpy as np
import random
import tensorflow as tf
from sklearn.metrics import mean_squared_error as my_mse

class stopAtLossValue(tf.keras.callbacks.Callback):
        def on_epoch_end(self, batch, logs={}):
            eps = 0.00001 
            if logs.get('loss') <= eps:
                 self.model.stop_training = True

model = tf.keras.Sequential([  
    tf.keras.layers.Flatten(input_shape=(x.shape[1],)),
    tf.keras.layers.Dense(8, activation='relu'),
    tf.keras.layers.Dense(8, activation='relu'),
    tf.keras.layers.Dense(8, activation='relu'),
    tf.keras.layers.Dense(16, activation='relu'),
    tf.keras.layers.Dense(1) 
])
model.compile(loss='mse',optimizer = tf.keras.optimizers.Adam(learning_rate=0.001))   
model.fit(x,  y, epochs=1000, batch_size=1, verbose=1, callbacks=[stopAtLossValue()])

For example, when I run the code snippet, I reach the loss value desired after 112 epochs.

Epoch 111/1000
20/20 [==============================] - 0s 2ms/step - loss: 0.0294
Epoch 112/1000
20/20 [==============================] - 0s 315us/step - loss: 1.0666e-06
<keras.callbacks.History at 0x153a7b70d30>

Then, I call the predict() method and compute the loss myself. By the way, my loss function is just a vanilla minimum squared error (MSE). The value that I get is quite high. In fact, if I print the predictions, they seem to be pretty bad even though tf stopped the training due to having a low MSE.

my_mse(y,model.predict(x))
0.027716089