Datasets:
HenryAI
/
KerasCodeExamples.txt

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def on_epoch_end(self, epoch, logs=None):
print(\"Mean PSNR for epoch: %.2f\" % (np.mean(self.psnr)))
if epoch % 20 == 0:
prediction = upscale_image(self.model, self.test_img)
plot_results(prediction, \"epoch-\" + str(epoch), \"prediction\")
def on_test_batch_end(self, batch, logs=None):
self.psnr.append(10 * math.log10(1 / logs[\"loss\"]))
Define ModelCheckpoint and EarlyStopping callbacks.
early_stopping_callback = keras.callbacks.EarlyStopping(monitor=\"loss\", patience=10)
checkpoint_filepath = \"/tmp/checkpoint\"
model_checkpoint_callback = keras.callbacks.ModelCheckpoint(
filepath=checkpoint_filepath,
save_weights_only=True,
monitor=\"loss\",
mode=\"min\",
save_best_only=True,
)
model = get_model(upscale_factor=upscale_factor, channels=1)
model.summary()
callbacks = [ESPCNCallback(), early_stopping_callback, model_checkpoint_callback]
loss_fn = keras.losses.MeanSquaredError()
optimizer = keras.optimizers.Adam(learning_rate=0.001)
Model: \"model\"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_1 (InputLayer) [(None, None, None, 1)] 0
_________________________________________________________________
conv2d (Conv2D) (None, None, None, 64) 1664
_________________________________________________________________
conv2d_1 (Conv2D) (None, None, None, 64) 36928
_________________________________________________________________
conv2d_2 (Conv2D) (None, None, None, 32) 18464
_________________________________________________________________
conv2d_3 (Conv2D) (None, None, None, 9) 2601
_________________________________________________________________
tf.nn.depth_to_space (TFOpLa (None, None, None, 1) 0
=================================================================
Total params: 59,657
Trainable params: 59,657
Non-trainable params: 0
_________________________________________________________________
Train the model
epochs = 100
model.compile(
optimizer=optimizer, loss=loss_fn,
)
model.fit(
train_ds, epochs=epochs, callbacks=callbacks, validation_data=valid_ds, verbose=2
)
# The model weights (that are considered the best) are loaded into the model.
model.load_weights(checkpoint_filepath)
WARNING: Logging before flag parsing goes to stderr.
W0828 11:01:31.262773 4528061888 callbacks.py:1270] Automatic model reloading for interrupted job was removed from the `ModelCheckpoint` callback in multi-worker mode, please use the [`keras.callbacks.experimental.BackupAndRestore`](/api/callbacks/backup_and_restore#backupandrestore-class) callback instead. See this tu...
Epoch 1/100
Mean PSNR for epoch: 22.03
png
50/50 - 14s - loss: 0.0259 - val_loss: 0.0063
Epoch 2/100
Mean PSNR for epoch: 24.55
50/50 - 13s - loss: 0.0049 - val_loss: 0.0034
Epoch 3/100
Mean PSNR for epoch: 25.57
50/50 - 13s - loss: 0.0035 - val_loss: 0.0029
Epoch 4/100
Mean PSNR for epoch: 26.35
50/50 - 13s - loss: 0.0031 - val_loss: 0.0026
Epoch 5/100
Mean PSNR for epoch: 25.88
50/50 - 13s - loss: 0.0029 - val_loss: 0.0026
Epoch 6/100
Mean PSNR for epoch: 26.23
50/50 - 13s - loss: 0.0030 - val_loss: 0.0025
Epoch 7/100
Mean PSNR for epoch: 26.30
50/50 - 13s - loss: 0.0028 - val_loss: 0.0025
Epoch 8/100
Mean PSNR for epoch: 26.27
50/50 - 13s - loss: 0.0028 - val_loss: 0.0025
Epoch 9/100
Mean PSNR for epoch: 26.38
50/50 - 12s - loss: 0.0028 - val_loss: 0.0025
Epoch 10/100
Mean PSNR for epoch: 26.25
50/50 - 12s - loss: 0.0027 - val_loss: 0.0024
Epoch 11/100
Mean PSNR for epoch: 26.19
50/50 - 12s - loss: 0.0027 - val_loss: 0.0025
Epoch 12/100