diff --git a/examples/vision/forwardforward.py b/examples/vision/forwardforward.py
index 6032eeeb41..ee0d8573c3 100644
--- a/examples/vision/forwardforward.py
+++ b/examples/vision/forwardforward.py
@@ -2,7 +2,7 @@
Title: Using the Forward-Forward Algorithm for Image Classification
Author: [Suvaditya Mukherjee](https://twitter.com/halcyonrayes)
Date created: 2023/01/08
-Last modified: 2023/01/08
+Last modified: 2024/09/17
Description: Training a Dense-layer model using the Forward-Forward algorithm.
Accelerator: GPU
"""
@@ -59,9 +59,13 @@
"""
## Setup imports
"""
+import os
+
+os.environ["KERAS_BACKEND"] = "tensorflow"
import tensorflow as tf
-from tensorflow import keras
+import keras
+from keras import ops
import numpy as np
import matplotlib.pyplot as plt
from sklearn.metrics import accuracy_score
@@ -143,7 +147,7 @@ class FFDense(keras.layers.Layer):
def __init__(
self,
units,
- optimizer,
+ init_optimizer,
loss_metric,
num_epochs=50,
use_bias=True,
@@ -163,7 +167,7 @@ def __init__(
bias_regularizer=bias_regularizer,
)
self.relu = keras.layers.ReLU()
- self.optimizer = optimizer
+ self.optimizer = init_optimizer()
self.loss_metric = loss_metric
self.threshold = 1.5
self.num_epochs = num_epochs
@@ -172,7 +176,7 @@ def __init__(
# layer.
def call(self, x):
- x_norm = tf.norm(x, ord=2, axis=1, keepdims=True)
+ x_norm = ops.norm(x, ord=2, axis=1, keepdims=True)
x_norm = x_norm + 1e-4
x_dir = x / x_norm
res = self.dense(x_dir)
@@ -192,22 +196,24 @@ def call(self, x):
def forward_forward(self, x_pos, x_neg):
for i in range(self.num_epochs):
with tf.GradientTape() as tape:
- g_pos = tf.math.reduce_mean(tf.math.pow(self.call(x_pos), 2), 1)
- g_neg = tf.math.reduce_mean(tf.math.pow(self.call(x_neg), 2), 1)
+ g_pos = ops.mean(ops.power(self.call(x_pos), 2), 1)
+ g_neg = ops.mean(ops.power(self.call(x_neg), 2), 1)
- loss = tf.math.log(
+ loss = ops.log(
1
- + tf.math.exp(
- tf.concat([-g_pos + self.threshold, g_neg - self.threshold], 0)
+ + ops.exp(
+ ops.concatenate(
+ [-g_pos + self.threshold, g_neg - self.threshold], 0
+ )
)
)
- mean_loss = tf.cast(tf.math.reduce_mean(loss), tf.float32)
+ mean_loss = ops.cast(ops.mean(loss), dtype="float32")
self.loss_metric.update_state([mean_loss])
gradients = tape.gradient(mean_loss, self.dense.trainable_weights)
self.optimizer.apply_gradients(zip(gradients, self.dense.trainable_weights))
return (
- tf.stop_gradient(self.call(x_pos)),
- tf.stop_gradient(self.call(x_neg)),
+ ops.stop_gradient(self.call(x_pos)),
+ ops.stop_gradient(self.call(x_neg)),
self.loss_metric.result(),
)
@@ -248,25 +254,24 @@ class FFNetwork(keras.Model):
# the `Adam` optimizer with a default learning rate of 0.03 as that was
# found to be the best rate after experimentation.
# Loss is tracked using `loss_var` and `loss_count` variables.
- # Use legacy optimizer for Layer Optimizer to fix issue
- # https://github.com/keras-team/keras-io/issues/1241
def __init__(
self,
dims,
- layer_optimizer=keras.optimizers.legacy.Adam(learning_rate=0.03),
+ init_layer_optimizer=lambda: keras.optimizers.Adam(learning_rate=0.03),
**kwargs,
):
super().__init__(**kwargs)
- self.layer_optimizer = layer_optimizer
- self.loss_var = tf.Variable(0.0, trainable=False, dtype=tf.float32)
- self.loss_count = tf.Variable(0.0, trainable=False, dtype=tf.float32)
+ self.init_layer_optimizer = init_layer_optimizer
+ self.loss_var = keras.Variable(0.0, trainable=False, dtype="float32")
+ self.loss_count = keras.Variable(0.0, trainable=False, dtype="float32")
self.layer_list = [keras.Input(shape=(dims[0],))]
+ self.metrics_built = False
for d in range(len(dims) - 1):
self.layer_list += [
FFDense(
dims[d + 1],
- optimizer=self.layer_optimizer,
+ init_optimizer=self.init_layer_optimizer,
loss_metric=keras.metrics.Mean(),
)
]
@@ -280,9 +285,9 @@ def __init__(
@tf.function(reduce_retracing=True)
def overlay_y_on_x(self, data):
X_sample, y_sample = data
- max_sample = tf.reduce_max(X_sample, axis=0, keepdims=True)
- max_sample = tf.cast(max_sample, dtype=tf.float64)
- X_zeros = tf.zeros([10], dtype=tf.float64)
+ max_sample = ops.amax(X_sample, axis=0, keepdims=True)
+ max_sample = ops.cast(max_sample, dtype="float64")
+ X_zeros = ops.zeros([10], dtype="float64")
X_update = xla.dynamic_update_slice(X_zeros, max_sample, [y_sample])
X_sample = xla.dynamic_update_slice(X_sample, X_update, [0])
return X_sample, y_sample
@@ -297,25 +302,23 @@ def overlay_y_on_x(self, data):
@tf.function(reduce_retracing=True)
def predict_one_sample(self, x):
goodness_per_label = []
- x = tf.reshape(x, [tf.shape(x)[0] * tf.shape(x)[1]])
+ x = ops.reshape(x, [ops.shape(x)[0] * ops.shape(x)[1]])
for label in range(10):
h, label = self.overlay_y_on_x(data=(x, label))
- h = tf.reshape(h, [-1, tf.shape(h)[0]])
+ h = ops.reshape(h, [-1, ops.shape(h)[0]])
goodness = []
for layer_idx in range(1, len(self.layer_list)):
layer = self.layer_list[layer_idx]
h = layer(h)
- goodness += [tf.math.reduce_mean(tf.math.pow(h, 2), 1)]
- goodness_per_label += [
- tf.expand_dims(tf.reduce_sum(goodness, keepdims=True), 1)
- ]
+ goodness += [ops.mean(ops.power(h, 2), 1)]
+ goodness_per_label += [ops.expand_dims(ops.sum(goodness, keepdims=True), 1)]
goodness_per_label = tf.concat(goodness_per_label, 1)
- return tf.cast(tf.argmax(goodness_per_label, 1), tf.float64)
+ return ops.cast(ops.argmax(goodness_per_label, 1), dtype="float64")
def predict(self, data):
x = data
preds = list()
- preds = tf.map_fn(fn=self.predict_one_sample, elems=x)
+ preds = ops.vectorized_map(self.predict_one_sample, x)
return np.asarray(preds, dtype=int)
# This custom `train_step` function overrides the internal `train_step`
@@ -328,17 +331,26 @@ def predict(self, data):
# the Forward-Forward computation on it. The returned loss is the final
# loss value over all the layers.
- @tf.function(jit_compile=True)
+ @tf.function(jit_compile=False)
def train_step(self, data):
x, y = data
+ if not self.metrics_built:
+ # build metrics to ensure they can be queried without erroring out.
+ # We can't update the metrics' state, as we would usually do, since
+ # we do not perform predictions within the train step
+ for metric in self.metrics:
+ if hasattr(metric, "build"):
+ metric.build(y, y)
+ self.metrics_built = True
+
# Flatten op
- x = tf.reshape(x, [-1, tf.shape(x)[1] * tf.shape(x)[2]])
+ x = ops.reshape(x, [-1, ops.shape(x)[1] * ops.shape(x)[2]])
- x_pos, y = tf.map_fn(fn=self.overlay_y_on_x, elems=(x, y))
+ x_pos, y = ops.vectorized_map(self.overlay_y_on_x, (x, y))
random_y = tf.random.shuffle(y)
- x_neg, y = tf.map_fn(fn=self.overlay_y_on_x, elems=(x, random_y))
+ x_neg, y = tf.map_fn(self.overlay_y_on_x, (x, random_y))
h_pos, h_neg = x_pos, x_neg
@@ -351,7 +363,7 @@ def train_step(self, data):
else:
print(f"Passing layer {idx+1} now : ")
x = layer(x)
- mean_res = tf.math.divide(self.loss_var, self.loss_count)
+ mean_res = ops.divide(self.loss_var, self.loss_count)
return {"FinalLoss": mean_res}
@@ -386,8 +398,8 @@ def train_step(self, data):
model.compile(
optimizer=keras.optimizers.Adam(learning_rate=0.03),
loss="mse",
- jit_compile=True,
- metrics=[keras.metrics.Mean()],
+ jit_compile=False,
+ metrics=[],
)
epochs = 250
@@ -400,7 +412,7 @@ def train_step(self, data):
test set. We calculate the Accuracy Score to understand the results closely.
"""
-preds = model.predict(tf.convert_to_tensor(x_test))
+preds = model.predict(ops.convert_to_tensor(x_test))
preds = preds.reshape((preds.shape[0], preds.shape[1]))
diff --git a/examples/vision/img/forwardforward/forwardforward_15_1.png b/examples/vision/img/forwardforward/forwardforward_15_1.png
index 0e95f8e1b8..7d29e9a8a2 100644
Binary files a/examples/vision/img/forwardforward/forwardforward_15_1.png and b/examples/vision/img/forwardforward/forwardforward_15_1.png differ
diff --git a/examples/vision/img/forwardforward/forwardforward_5_1.png b/examples/vision/img/forwardforward/forwardforward_5_1.png
index 8d7b00b6c0..6b63c595ea 100644
Binary files a/examples/vision/img/forwardforward/forwardforward_5_1.png and b/examples/vision/img/forwardforward/forwardforward_5_1.png differ
diff --git a/examples/vision/ipynb/forwardforward.ipynb b/examples/vision/ipynb/forwardforward.ipynb
index 372e92a965..d0837d39b8 100644
--- a/examples/vision/ipynb/forwardforward.ipynb
+++ b/examples/vision/ipynb/forwardforward.ipynb
@@ -1,7 +1,6 @@
{
"cells": [
{
- "attachments": {},
"cell_type": "markdown",
"metadata": {
"colab_type": "text"
@@ -11,7 +10,7 @@
"\n",
"**Author:** [Suvaditya Mukherjee](https://twitter.com/halcyonrayes)
\n",
"**Date created:** 2023/01/08
\n",
- "**Last modified:** 2023/01/08
\n",
+ "**Last modified:** 2024/09/17
\n",
"**Description:** Training a Dense-layer model using the Forward-Forward algorithm."
]
},
@@ -80,14 +79,19 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 0,
"metadata": {
"colab_type": "code"
},
"outputs": [],
"source": [
+ "import os\n",
+ "\n",
+ "os.environ[\"KERAS_BACKEND\"] = \"tensorflow\"\n",
+ "\n",
"import tensorflow as tf\n",
- "from tensorflow import keras\n",
+ "import keras\n",
+ "from keras import ops\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from sklearn.metrics import accuracy_score\n",
@@ -113,7 +117,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 0,
"metadata": {
"colab_type": "code"
},
@@ -180,7 +184,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 0,
"metadata": {
"colab_type": "code"
},
@@ -197,7 +201,7 @@
" def __init__(\n",
" self,\n",
" units,\n",
- " optimizer,\n",
+ " init_optimizer,\n",
" loss_metric,\n",
" num_epochs=50,\n",
" use_bias=True,\n",
@@ -217,7 +221,7 @@
" bias_regularizer=bias_regularizer,\n",
" )\n",
" self.relu = keras.layers.ReLU()\n",
- " self.optimizer = optimizer\n",
+ " self.optimizer = init_optimizer()\n",
" self.loss_metric = loss_metric\n",
" self.threshold = 1.5\n",
" self.num_epochs = num_epochs\n",
@@ -226,7 +230,7 @@
" # layer.\n",
"\n",
" def call(self, x):\n",
- " x_norm = tf.norm(x, ord=2, axis=1, keepdims=True)\n",
+ " x_norm = ops.norm(x, ord=2, axis=1, keepdims=True)\n",
" x_norm = x_norm + 1e-4\n",
" x_dir = x / x_norm\n",
" res = self.dense(x_dir)\n",
@@ -246,24 +250,27 @@
" def forward_forward(self, x_pos, x_neg):\n",
" for i in range(self.num_epochs):\n",
" with tf.GradientTape() as tape:\n",
- " g_pos = tf.math.reduce_mean(tf.math.pow(self.call(x_pos), 2), 1)\n",
- " g_neg = tf.math.reduce_mean(tf.math.pow(self.call(x_neg), 2), 1)\n",
+ " g_pos = ops.mean(ops.power(self.call(x_pos), 2), 1)\n",
+ " g_neg = ops.mean(ops.power(self.call(x_neg), 2), 1)\n",
"\n",
- " loss = tf.math.log(\n",
+ " loss = ops.log(\n",
" 1\n",
- " + tf.math.exp(\n",
- " tf.concat([-g_pos + self.threshold, g_neg - self.threshold], 0)\n",
+ " + ops.exp(\n",
+ " ops.concatenate(\n",
+ " [-g_pos + self.threshold, g_neg - self.threshold], 0\n",
+ " )\n",
" )\n",
" )\n",
- " mean_loss = tf.cast(tf.math.reduce_mean(loss), tf.float32)\n",
+ " mean_loss = ops.cast(ops.mean(loss), dtype=\"float32\")\n",
" self.loss_metric.update_state([mean_loss])\n",
" gradients = tape.gradient(mean_loss, self.dense.trainable_weights)\n",
" self.optimizer.apply_gradients(zip(gradients, self.dense.trainable_weights))\n",
" return (\n",
- " tf.stop_gradient(self.call(x_pos)),\n",
- " tf.stop_gradient(self.call(x_neg)),\n",
+ " ops.stop_gradient(self.call(x_pos)),\n",
+ " ops.stop_gradient(self.call(x_neg)),\n",
" self.loss_metric.result(),\n",
- " )\n"
+ " )\n",
+ ""
]
},
{
@@ -295,7 +302,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 0,
"metadata": {
"colab_type": "code"
},
@@ -315,25 +322,24 @@
" # the `Adam` optimizer with a default learning rate of 0.03 as that was\n",
" # found to be the best rate after experimentation.\n",
" # Loss is tracked using `loss_var` and `loss_count` variables.\n",
- " # Use legacy optimizer for Layer Optimizer to fix issue\n",
- " # https://github.com/keras-team/keras-io/issues/1241\n",
"\n",
" def __init__(\n",
" self,\n",
" dims,\n",
- " layer_optimizer=keras.optimizers.legacy.Adam(learning_rate=0.03),\n",
+ " init_layer_optimizer=lambda: keras.optimizers.Adam(learning_rate=0.03),\n",
" **kwargs,\n",
" ):\n",
" super().__init__(**kwargs)\n",
- " self.layer_optimizer = layer_optimizer\n",
- " self.loss_var = tf.Variable(0.0, trainable=False, dtype=tf.float32)\n",
- " self.loss_count = tf.Variable(0.0, trainable=False, dtype=tf.float32)\n",
+ " self.init_layer_optimizer = init_layer_optimizer\n",
+ " self.loss_var = keras.Variable(0.0, trainable=False, dtype=\"float32\")\n",
+ " self.loss_count = keras.Variable(0.0, trainable=False, dtype=\"float32\")\n",
" self.layer_list = [keras.Input(shape=(dims[0],))]\n",
+ " self.metrics_built = False\n",
" for d in range(len(dims) - 1):\n",
" self.layer_list += [\n",
" FFDense(\n",
" dims[d + 1],\n",
- " optimizer=self.layer_optimizer,\n",
+ " init_optimizer=self.init_layer_optimizer,\n",
" loss_metric=keras.metrics.Mean(),\n",
" )\n",
" ]\n",
@@ -347,9 +353,9 @@
" @tf.function(reduce_retracing=True)\n",
" def overlay_y_on_x(self, data):\n",
" X_sample, y_sample = data\n",
- " max_sample = tf.reduce_max(X_sample, axis=0, keepdims=True)\n",
- " max_sample = tf.cast(max_sample, dtype=tf.float64)\n",
- " X_zeros = tf.zeros([10], dtype=tf.float64)\n",
+ " max_sample = ops.amax(X_sample, axis=0, keepdims=True)\n",
+ " max_sample = ops.cast(max_sample, dtype=\"float64\")\n",
+ " X_zeros = ops.zeros([10], dtype=\"float64\")\n",
" X_update = xla.dynamic_update_slice(X_zeros, max_sample, [y_sample])\n",
" X_sample = xla.dynamic_update_slice(X_sample, X_update, [0])\n",
" return X_sample, y_sample\n",
@@ -364,25 +370,23 @@
" @tf.function(reduce_retracing=True)\n",
" def predict_one_sample(self, x):\n",
" goodness_per_label = []\n",
- " x = tf.reshape(x, [tf.shape(x)[0] * tf.shape(x)[1]])\n",
+ " x = ops.reshape(x, [ops.shape(x)[0] * ops.shape(x)[1]])\n",
" for label in range(10):\n",
" h, label = self.overlay_y_on_x(data=(x, label))\n",
- " h = tf.reshape(h, [-1, tf.shape(h)[0]])\n",
+ " h = ops.reshape(h, [-1, ops.shape(h)[0]])\n",
" goodness = []\n",
" for layer_idx in range(1, len(self.layer_list)):\n",
" layer = self.layer_list[layer_idx]\n",
" h = layer(h)\n",
- " goodness += [tf.math.reduce_mean(tf.math.pow(h, 2), 1)]\n",
- " goodness_per_label += [\n",
- " tf.expand_dims(tf.reduce_sum(goodness, keepdims=True), 1)\n",
- " ]\n",
+ " goodness += [ops.mean(ops.power(h, 2), 1)]\n",
+ " goodness_per_label += [ops.expand_dims(ops.sum(goodness, keepdims=True), 1)]\n",
" goodness_per_label = tf.concat(goodness_per_label, 1)\n",
- " return tf.cast(tf.argmax(goodness_per_label, 1), tf.float64)\n",
+ " return ops.cast(ops.argmax(goodness_per_label, 1), dtype=\"float64\")\n",
"\n",
" def predict(self, data):\n",
" x = data\n",
" preds = list()\n",
- " preds = tf.map_fn(fn=self.predict_one_sample, elems=x)\n",
+ " preds = ops.vectorized_map(self.predict_one_sample, x)\n",
" return np.asarray(preds, dtype=int)\n",
"\n",
" # This custom `train_step` function overrides the internal `train_step`\n",
@@ -395,17 +399,26 @@
" # the Forward-Forward computation on it. The returned loss is the final\n",
" # loss value over all the layers.\n",
"\n",
- " @tf.function(jit_compile=True)\n",
+ " @tf.function(jit_compile=False)\n",
" def train_step(self, data):\n",
" x, y = data\n",
"\n",
+ " if not self.metrics_built:\n",
+ " # build metrics to ensure they can be queried without erroring out.\n",
+ " # We can't update the metrics' state, as we would usually do, since\n",
+ " # we do not perform predictions within the train step\n",
+ " for metric in self.metrics:\n",
+ " if hasattr(metric, \"build\"):\n",
+ " metric.build(y, y)\n",
+ " self.metrics_built = True\n",
+ "\n",
" # Flatten op\n",
- " x = tf.reshape(x, [-1, tf.shape(x)[1] * tf.shape(x)[2]])\n",
+ " x = ops.reshape(x, [-1, ops.shape(x)[1] * ops.shape(x)[2]])\n",
"\n",
- " x_pos, y = tf.map_fn(fn=self.overlay_y_on_x, elems=(x, y))\n",
+ " x_pos, y = ops.vectorized_map(self.overlay_y_on_x, (x, y))\n",
"\n",
" random_y = tf.random.shuffle(y)\n",
- " x_neg, y = tf.map_fn(fn=self.overlay_y_on_x, elems=(x, random_y))\n",
+ " x_neg, y = tf.map_fn(self.overlay_y_on_x, (x, random_y))\n",
"\n",
" h_pos, h_neg = x_pos, x_neg\n",
"\n",
@@ -418,8 +431,9 @@
" else:\n",
" print(f\"Passing layer {idx+1} now : \")\n",
" x = layer(x)\n",
- " mean_res = tf.math.divide(self.loss_var, self.loss_count)\n",
- " return {\"FinalLoss\": mean_res}\n"
+ " mean_res = ops.divide(self.loss_var, self.loss_count)\n",
+ " return {\"FinalLoss\": mean_res}\n",
+ ""
]
},
{
@@ -436,7 +450,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 0,
"metadata": {
"colab_type": "code"
},
@@ -469,7 +483,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 0,
"metadata": {
"colab_type": "code"
},
@@ -480,8 +494,8 @@
"model.compile(\n",
" optimizer=keras.optimizers.Adam(learning_rate=0.03),\n",
" loss=\"mse\",\n",
- " jit_compile=True,\n",
- " metrics=[keras.metrics.Mean()],\n",
+ " jit_compile=False,\n",
+ " metrics=[],\n",
")\n",
"\n",
"epochs = 250\n",
@@ -502,13 +516,13 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 0,
"metadata": {
"colab_type": "code"
},
"outputs": [],
"source": [
- "preds = model.predict(tf.convert_to_tensor(x_test))\n",
+ "preds = model.predict(ops.convert_to_tensor(x_test))\n",
"\n",
"preds = preds.reshape((preds.shape[0], preds.shape[1]))\n",
"\n",
@@ -577,4 +591,4 @@
},
"nbformat": 4,
"nbformat_minor": 0
-}
+}
\ No newline at end of file
diff --git a/examples/vision/md/forwardforward.md b/examples/vision/md/forwardforward.md
index 4fe88fd395..43c97d6166 100644
--- a/examples/vision/md/forwardforward.md
+++ b/examples/vision/md/forwardforward.md
@@ -2,7 +2,7 @@
**Author:** [Suvaditya Mukherjee](https://twitter.com/halcyonrayes)
**Date created:** 2023/01/08
-**Last modified:** 2023/01/08
+**Last modified:** 2024/09/17
**Description:** Training a Dense-layer model using the Forward-Forward algorithm.
@@ -63,8 +63,13 @@ a primer on how to do so:
```python
+import os
+
+os.environ["KERAS_BACKEND"] = "tensorflow"
+
import tensorflow as tf
-from tensorflow import keras
+import keras
+from keras import ops
import numpy as np
import matplotlib.pyplot as plt
from sklearn.metrics import accuracy_score
@@ -72,6 +77,7 @@ import random
from tensorflow.compiler.tf2xla.python import xla
```
+
---
## Load the dataset and visualize the data
@@ -108,13 +114,13 @@ plt.show()
```
-Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz
-11490434/11490434 [==============================] - 0s 0us/step
4 Random Training samples and labels
```
+
+
---
@@ -161,7 +167,7 @@ class FFDense(keras.layers.Layer):
def __init__(
self,
units,
- optimizer,
+ init_optimizer,
loss_metric,
num_epochs=50,
use_bias=True,
@@ -181,7 +187,7 @@ class FFDense(keras.layers.Layer):
bias_regularizer=bias_regularizer,
)
self.relu = keras.layers.ReLU()
- self.optimizer = optimizer
+ self.optimizer = init_optimizer()
self.loss_metric = loss_metric
self.threshold = 1.5
self.num_epochs = num_epochs
@@ -190,7 +196,7 @@ class FFDense(keras.layers.Layer):
# layer.
def call(self, x):
- x_norm = tf.norm(x, ord=2, axis=1, keepdims=True)
+ x_norm = ops.norm(x, ord=2, axis=1, keepdims=True)
x_norm = x_norm + 1e-4
x_dir = x / x_norm
res = self.dense(x_dir)
@@ -210,22 +216,24 @@ class FFDense(keras.layers.Layer):
def forward_forward(self, x_pos, x_neg):
for i in range(self.num_epochs):
with tf.GradientTape() as tape:
- g_pos = tf.math.reduce_mean(tf.math.pow(self.call(x_pos), 2), 1)
- g_neg = tf.math.reduce_mean(tf.math.pow(self.call(x_neg), 2), 1)
+ g_pos = ops.mean(ops.power(self.call(x_pos), 2), 1)
+ g_neg = ops.mean(ops.power(self.call(x_neg), 2), 1)
- loss = tf.math.log(
+ loss = ops.log(
1
- + tf.math.exp(
- tf.concat([-g_pos + self.threshold, g_neg - self.threshold], 0)
+ + ops.exp(
+ ops.concatenate(
+ [-g_pos + self.threshold, g_neg - self.threshold], 0
+ )
)
)
- mean_loss = tf.cast(tf.math.reduce_mean(loss), tf.float32)
+ mean_loss = ops.cast(ops.mean(loss), dtype="float32")
self.loss_metric.update_state([mean_loss])
gradients = tape.gradient(mean_loss, self.dense.trainable_weights)
self.optimizer.apply_gradients(zip(gradients, self.dense.trainable_weights))
return (
- tf.stop_gradient(self.call(x_pos)),
- tf.stop_gradient(self.call(x_neg)),
+ ops.stop_gradient(self.call(x_pos)),
+ ops.stop_gradient(self.call(x_neg)),
self.loss_metric.result(),
)
@@ -268,25 +276,24 @@ class FFNetwork(keras.Model):
# the `Adam` optimizer with a default learning rate of 0.03 as that was
# found to be the best rate after experimentation.
# Loss is tracked using `loss_var` and `loss_count` variables.
- # Use legacy optimizer for Layer Optimizer to fix issue
- # https://github.com/keras-team/keras-io/issues/1241
def __init__(
self,
dims,
- layer_optimizer=keras.optimizers.legacy.Adam(learning_rate=0.03),
+ init_layer_optimizer=lambda: keras.optimizers.Adam(learning_rate=0.03),
**kwargs,
):
super().__init__(**kwargs)
- self.layer_optimizer = layer_optimizer
- self.loss_var = tf.Variable(0.0, trainable=False, dtype=tf.float32)
- self.loss_count = tf.Variable(0.0, trainable=False, dtype=tf.float32)
+ self.init_layer_optimizer = init_layer_optimizer
+ self.loss_var = keras.Variable(0.0, trainable=False, dtype="float32")
+ self.loss_count = keras.Variable(0.0, trainable=False, dtype="float32")
self.layer_list = [keras.Input(shape=(dims[0],))]
+ self.metrics_built = False
for d in range(len(dims) - 1):
self.layer_list += [
FFDense(
dims[d + 1],
- optimizer=self.layer_optimizer,
+ init_optimizer=self.init_layer_optimizer,
loss_metric=keras.metrics.Mean(),
)
]
@@ -300,9 +307,9 @@ class FFNetwork(keras.Model):
@tf.function(reduce_retracing=True)
def overlay_y_on_x(self, data):
X_sample, y_sample = data
- max_sample = tf.reduce_max(X_sample, axis=0, keepdims=True)
- max_sample = tf.cast(max_sample, dtype=tf.float64)
- X_zeros = tf.zeros([10], dtype=tf.float64)
+ max_sample = ops.amax(X_sample, axis=0, keepdims=True)
+ max_sample = ops.cast(max_sample, dtype="float64")
+ X_zeros = ops.zeros([10], dtype="float64")
X_update = xla.dynamic_update_slice(X_zeros, max_sample, [y_sample])
X_sample = xla.dynamic_update_slice(X_sample, X_update, [0])
return X_sample, y_sample
@@ -317,25 +324,23 @@ class FFNetwork(keras.Model):
@tf.function(reduce_retracing=True)
def predict_one_sample(self, x):
goodness_per_label = []
- x = tf.reshape(x, [tf.shape(x)[0] * tf.shape(x)[1]])
+ x = ops.reshape(x, [ops.shape(x)[0] * ops.shape(x)[1]])
for label in range(10):
h, label = self.overlay_y_on_x(data=(x, label))
- h = tf.reshape(h, [-1, tf.shape(h)[0]])
+ h = ops.reshape(h, [-1, ops.shape(h)[0]])
goodness = []
for layer_idx in range(1, len(self.layer_list)):
layer = self.layer_list[layer_idx]
h = layer(h)
- goodness += [tf.math.reduce_mean(tf.math.pow(h, 2), 1)]
- goodness_per_label += [
- tf.expand_dims(tf.reduce_sum(goodness, keepdims=True), 1)
- ]
+ goodness += [ops.mean(ops.power(h, 2), 1)]
+ goodness_per_label += [ops.expand_dims(ops.sum(goodness, keepdims=True), 1)]
goodness_per_label = tf.concat(goodness_per_label, 1)
- return tf.cast(tf.argmax(goodness_per_label, 1), tf.float64)
+ return ops.cast(ops.argmax(goodness_per_label, 1), dtype="float64")
def predict(self, data):
x = data
preds = list()
- preds = tf.map_fn(fn=self.predict_one_sample, elems=x)
+ preds = ops.vectorized_map(self.predict_one_sample, x)
return np.asarray(preds, dtype=int)
# This custom `train_step` function overrides the internal `train_step`
@@ -348,17 +353,26 @@ class FFNetwork(keras.Model):
# the Forward-Forward computation on it. The returned loss is the final
# loss value over all the layers.
- @tf.function(jit_compile=True)
+ @tf.function(jit_compile=False)
def train_step(self, data):
x, y = data
+ if not self.metrics_built:
+ # build metrics to ensure they can be queried without erroring out.
+ # We can't update the metrics' state, as we would usually do, since
+ # we do not perform predictions within the train step
+ for metric in self.metrics:
+ if hasattr(metric, "build"):
+ metric.build(y, y)
+ self.metrics_built = True
+
# Flatten op
- x = tf.reshape(x, [-1, tf.shape(x)[1] * tf.shape(x)[2]])
+ x = ops.reshape(x, [-1, ops.shape(x)[1] * ops.shape(x)[2]])
- x_pos, y = tf.map_fn(fn=self.overlay_y_on_x, elems=(x, y))
+ x_pos, y = ops.vectorized_map(self.overlay_y_on_x, (x, y))
random_y = tf.random.shuffle(y)
- x_neg, y = tf.map_fn(fn=self.overlay_y_on_x, elems=(x, random_y))
+ x_neg, y = tf.map_fn(self.overlay_y_on_x, (x, random_y))
h_pos, h_neg = x_pos, x_neg
@@ -371,7 +385,7 @@ class FFNetwork(keras.Model):
else:
print(f"Passing layer {idx+1} now : ")
x = layer(x)
- mean_res = tf.math.divide(self.loss_var, self.loss_count)
+ mean_res = ops.divide(self.loss_var, self.loss_count)
return {"FinalLoss": mean_res}
```
@@ -396,6 +410,7 @@ train_dataset = train_dataset.batch(60000)
test_dataset = test_dataset.batch(10000)
```
+
---
## Fit the network and visualize results
@@ -410,8 +425,8 @@ model = FFNetwork(dims=[784, 500, 500])
model.compile(
optimizer=keras.optimizers.Adam(learning_rate=0.03),
loss="mse",
- jit_compile=True,
- metrics=[keras.metrics.Mean()],
+ jit_compile=False,
+ metrics=[],
)
epochs = 250
@@ -421,512 +436,766 @@ history = model.fit(train_dataset, epochs=epochs)
```
Epoch 1/250
+
Training layer 1 now :
+
Training layer 2 now :
+
Training layer 1 now :
+
Training layer 2 now :
-1/1 [==============================] - 72s 72s/step - FinalLoss: 0.7279
+1/1 ━━━━━━━━━━━━━━━━━━━━ 90s 90s/step - FinalLoss: 0.7247
+
Epoch 2/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.7082
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.7089
+
Epoch 3/250
-1/1 [==============================] - 6s 6s/step - FinalLoss: 0.7031
+ 1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.6978
+
Epoch 4/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.6806
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.6827
+
Epoch 5/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.6564
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.6644
+
Epoch 6/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.6333
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.6462
+
Epoch 7/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.6126
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.6290
+
Epoch 8/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.5946
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.6131
+
Epoch 9/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.5786
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.5986
+
Epoch 10/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.5644
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.5853
+
Epoch 11/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.5518
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.5731
+
Epoch 12/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.5405
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.5621
+
Epoch 13/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.5301
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.5519
+
Epoch 14/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.5207
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.5425
+
Epoch 15/250
-1/1 [==============================] - 6s 6s/step - FinalLoss: 0.5122
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.5338
+
Epoch 16/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.5044
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.5259
+
Epoch 17/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.4972
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.5186
+
Epoch 18/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.4906
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.5117
+
Epoch 19/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.4845
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.5052
+
Epoch 20/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.4787
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.4992
+
Epoch 21/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.4734
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.4935
+
Epoch 22/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.4685
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.4883
+
Epoch 23/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.4639
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.4833
+
Epoch 24/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.4596
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.4786
+
Epoch 25/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.4555
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.4741
+
Epoch 26/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.4516
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.4698
+
Epoch 27/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.4479
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.4658
+
Epoch 28/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.4445
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.4620
+
Epoch 29/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.4411
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.4584
+
Epoch 30/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.4380
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.4550
+
Epoch 31/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.4350
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.4517
+
Epoch 32/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.4322
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.4486
+
Epoch 33/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.4295
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.4456
+
Epoch 34/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.4269
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.4429
+
Epoch 35/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.4245
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.4401
+
Epoch 36/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.4222
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.4375
+
Epoch 37/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.4199
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.4350
+
Epoch 38/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.4178
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.4325
+
Epoch 39/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.4157
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.4302
+
Epoch 40/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.4136
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.4279
+
Epoch 41/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.4117
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.4258
+
Epoch 42/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.4098
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.4236
+
Epoch 43/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.4079
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.4216
+
Epoch 44/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.4062
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.4197
+
Epoch 45/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.4045
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.4177
+
Epoch 46/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.4028
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.4159
+
Epoch 47/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.4012
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.4141
+
Epoch 48/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3996
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.4124
+
Epoch 49/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3982
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.4107
+
Epoch 50/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3967
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.4090
+
Epoch 51/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3952
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.4074
+
Epoch 52/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3938
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.4059
+
Epoch 53/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3925
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.4044
+
Epoch 54/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3912
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.4030
+
Epoch 55/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3899
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.4016
+
Epoch 56/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3886
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.4002
+
Epoch 57/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3874
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.3988
+
Epoch 58/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3862
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.3975
+
Epoch 59/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3851
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.3962
+
Epoch 60/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3840
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.3950
+
Epoch 61/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3829
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.3938
+
Epoch 62/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3818
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.3926
+
Epoch 63/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3807
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.3914
+
Epoch 64/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3797
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.3903
+
Epoch 65/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3787
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.3891
+
Epoch 66/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3777
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.3880
+
Epoch 67/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3767
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.3869
+
Epoch 68/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3758
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.3859
+
Epoch 69/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3748
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.3849
+
Epoch 70/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3739
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.3839
+
Epoch 71/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3730
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.3829
+
Epoch 72/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3721
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.3819
+
Epoch 73/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3712
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.3810
+
Epoch 74/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3704
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.3801
+
Epoch 75/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3695
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.3792
+
Epoch 76/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3688
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.3783
+
Epoch 77/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3680
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.3774
+
Epoch 78/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3671
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.3765
+
Epoch 79/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3664
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.3757
+
Epoch 80/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3656
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.3748
+
Epoch 81/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3648
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.3740
+
Epoch 82/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3641
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.3732
+
Epoch 83/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3634
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.3723
+
Epoch 84/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3627
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.3715
+
Epoch 85/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3620
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.3708
+
Epoch 86/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3613
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.3700
+
Epoch 87/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3606
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.3692
+
Epoch 88/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3599
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.3685
+
Epoch 89/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3593
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.3677
+
Epoch 90/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3586
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.3670
+
Epoch 91/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3580
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.3663
+
Epoch 92/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3574
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.3656
+
Epoch 93/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3568
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.3649
+
Epoch 94/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3561
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.3642
+
Epoch 95/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3555
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.3635
+
Epoch 96/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3549
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.3629
+
Epoch 97/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3544
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.3622
+
Epoch 98/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3538
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.3616
+
Epoch 99/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3532
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.3610
+
Epoch 100/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3526
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.3603
+
Epoch 101/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3521
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.3597
+
Epoch 102/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3515
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.3591
+
Epoch 103/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3510
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.3585
+
Epoch 104/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3505
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.3579
+
Epoch 105/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3499
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.3573
+
Epoch 106/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3494
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.3567
+
Epoch 107/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3489
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.3562
+
Epoch 108/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3484
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.3556
+
Epoch 109/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3478
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.3550
+
Epoch 110/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3474
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.3545
+
Epoch 111/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3468
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.3539
+
Epoch 112/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3464
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.3534
+
Epoch 113/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3459
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.3529
+
Epoch 114/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3454
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.3524
+
Epoch 115/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3450
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.3519
+
Epoch 116/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3445
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.3513
+
Epoch 117/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3440
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.3508
+
Epoch 118/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3436
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.3503
+
Epoch 119/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3432
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.3498
+
Epoch 120/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3427
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.3493
+
Epoch 121/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3423
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.3488
+
Epoch 122/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3419
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.3484
+
Epoch 123/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3414
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.3479
+
Epoch 124/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3410
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.3474
+
Epoch 125/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3406
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.3470
+
Epoch 126/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3402
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.3465
+
Epoch 127/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3398
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.3461
+
Epoch 128/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3394
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.3456
+
Epoch 129/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3390
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.3452
+
Epoch 130/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3386
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.3447
+
Epoch 131/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3382
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.3443
+
Epoch 132/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3378
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.3439
+
Epoch 133/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3375
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.3435
+
Epoch 134/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3371
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.3430
+
Epoch 135/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3368
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.3426
+
Epoch 136/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3364
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.3422
+
Epoch 137/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3360
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.3418
+
Epoch 138/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3357
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.3414
+
Epoch 139/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3353
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.3411
+
Epoch 140/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3350
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.3407
+
Epoch 141/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3346
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.3403
+
Epoch 142/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3343
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.3399
+
Epoch 143/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3339
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.3395
+
Epoch 144/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3336
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.3391
+
Epoch 145/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3333
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.3387
+
Epoch 146/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3329
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.3384
+
Epoch 147/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3326
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.3380
+
Epoch 148/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3323
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.3376
+
Epoch 149/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3320
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.3373
+
Epoch 150/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3317
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.3369
+
Epoch 151/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3313
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.3366
+
Epoch 152/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3310
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.3362
+
Epoch 153/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3307
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.3359
+
Epoch 154/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3304
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.3355
+
Epoch 155/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3302
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.3352
+
Epoch 156/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3299
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.3349
+
Epoch 157/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3296
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.3346
+
Epoch 158/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3293
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.3342
+
Epoch 159/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3290
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.3339
+
Epoch 160/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3287
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.3336
+
Epoch 161/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3284
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.3333
+
Epoch 162/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3281
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.3330
+
Epoch 163/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3279
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.3327
+
Epoch 164/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3276
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.3324
+
Epoch 165/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3273
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.3321
+
Epoch 166/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3270
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.3318
+
Epoch 167/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3268
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.3315
+
Epoch 168/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3265
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.3312
+
Epoch 169/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3262
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.3309
+
Epoch 170/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3260
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.3306
+
Epoch 171/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3257
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.3303
+
Epoch 172/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3255
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.3301
+
Epoch 173/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3252
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.3298
+
Epoch 174/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3250
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.3295
+
Epoch 175/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3247
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.3292
+
Epoch 176/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3244
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.3289
+
Epoch 177/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3242
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.3287
+
Epoch 178/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3240
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.3284
+
Epoch 179/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3237
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.3281
+
Epoch 180/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3235
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.3279
+
Epoch 181/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3232
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.3276
+
Epoch 182/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3230
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.3273
+
Epoch 183/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3228
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.3271
+
Epoch 184/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3225
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.3268
+
Epoch 185/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3223
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.3266
+
Epoch 186/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3221
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.3263
+
Epoch 187/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3219
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.3261
+
Epoch 188/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3216
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.3259
+
Epoch 189/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3214
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.3256
+
Epoch 190/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3212
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.3254
+
Epoch 191/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3210
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.3251
+
Epoch 192/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3208
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.3249
+
Epoch 193/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3205
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.3247
+
Epoch 194/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3203
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.3244
+
Epoch 195/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3201
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.3242
+
Epoch 196/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3199
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.3240
+
Epoch 197/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3197
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.3238
+
Epoch 198/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3195
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.3235
+
Epoch 199/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3193
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.3233
+
Epoch 200/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3191
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.3231
+
Epoch 201/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3189
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.3228
+
Epoch 202/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3187
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.3226
+
Epoch 203/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3185
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.3224
+
Epoch 204/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3183
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.3222
+
Epoch 205/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3181
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.3220
+
Epoch 206/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3179
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.3217
+
Epoch 207/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3177
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.3215
+
Epoch 208/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3175
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.3213
+
Epoch 209/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3174
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.3211
+
Epoch 210/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3172
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.3209
+
Epoch 211/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3170
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.3207
+
Epoch 212/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3168
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.3205
+
Epoch 213/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3166
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.3203
+
Epoch 214/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3165
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.3201
+
Epoch 215/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3163
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.3199
+
Epoch 216/250
-1/1 [==============================] - 6s 6s/step - FinalLoss: 0.3161
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.3197
+
Epoch 217/250
-1/1 [==============================] - 6s 6s/step - FinalLoss: 0.3159
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.3195
+
Epoch 218/250
-1/1 [==============================] - 6s 6s/step - FinalLoss: 0.3157
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.3193
+
Epoch 219/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3155
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.3191
+
Epoch 220/250
-1/1 [==============================] - 5s 5s/step - FinalLoss: 0.3154
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.3190
+
Epoch 221/250
-1/1 [==============================] - 6s 6s/step - FinalLoss: 0.3152
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.3188
+
Epoch 222/250
-1/1 [==============================] - 6s 6s/step - FinalLoss: 0.3150
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.3186
+
Epoch 223/250
-1/1 [==============================] - 6s 6s/step - FinalLoss: 0.3148
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.3184
+
Epoch 224/250
-1/1 [==============================] - 6s 6s/step - FinalLoss: 0.3147
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.3182
+
Epoch 225/250
-1/1 [==============================] - 6s 6s/step - FinalLoss: 0.3145
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.3180
+
Epoch 226/250
-1/1 [==============================] - 6s 6s/step - FinalLoss: 0.3143
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.3179
+
Epoch 227/250
-1/1 [==============================] - 6s 6s/step - FinalLoss: 0.3142
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.3177
+
Epoch 228/250
-1/1 [==============================] - 6s 6s/step - FinalLoss: 0.3140
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.3175
+
Epoch 229/250
-1/1 [==============================] - 6s 6s/step - FinalLoss: 0.3139
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.3173
+
Epoch 230/250
-1/1 [==============================] - 6s 6s/step - FinalLoss: 0.3137
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.3171
+
Epoch 231/250
-1/1 [==============================] - 6s 6s/step - FinalLoss: 0.3135
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.3170
+
Epoch 232/250
-1/1 [==============================] - 6s 6s/step - FinalLoss: 0.3134
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.3168
+
Epoch 233/250
-1/1 [==============================] - 6s 6s/step - FinalLoss: 0.3132
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.3166
+
Epoch 234/250
-1/1 [==============================] - 6s 6s/step - FinalLoss: 0.3131
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.3164
+
Epoch 235/250
-1/1 [==============================] - 6s 6s/step - FinalLoss: 0.3129
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.3163
+
Epoch 236/250
-1/1 [==============================] - 6s 6s/step - FinalLoss: 0.3127
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.3161
+
Epoch 237/250
-1/1 [==============================] - 6s 6s/step - FinalLoss: 0.3126
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.3159
+
Epoch 238/250
-1/1 [==============================] - 6s 6s/step - FinalLoss: 0.3124
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.3158
+
Epoch 239/250
-1/1 [==============================] - 6s 6s/step - FinalLoss: 0.3123
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.3156
+
Epoch 240/250
-1/1 [==============================] - 6s 6s/step - FinalLoss: 0.3121
+1/1 ━━━━━━━━━━━━━━━━━━━━ 41s 41s/step - FinalLoss: 0.3154
+
Epoch 241/250
-1/1 [==============================] - 6s 6s/step - FinalLoss: 0.3120
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.3152
+
Epoch 242/250
-1/1 [==============================] - 6s 6s/step - FinalLoss: 0.3118
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.3151
+
Epoch 243/250
-1/1 [==============================] - 6s 6s/step - FinalLoss: 0.3117
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.3149
+
Epoch 244/250
-1/1 [==============================] - 6s 6s/step - FinalLoss: 0.3116
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.3148
+
Epoch 245/250
-1/1 [==============================] - 6s 6s/step - FinalLoss: 0.3114
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.3146
+
Epoch 246/250
-1/1 [==============================] - 6s 6s/step - FinalLoss: 0.3113
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.3145
+
Epoch 247/250
-1/1 [==============================] - 6s 6s/step - FinalLoss: 0.3111
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.3143
+
Epoch 248/250
-1/1 [==============================] - 6s 6s/step - FinalLoss: 0.3110
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.3141
+
Epoch 249/250
-1/1 [==============================] - 6s 6s/step - FinalLoss: 0.3108
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.3140
+
Epoch 250/250
-1/1 [==============================] - 6s 6s/step - FinalLoss: 0.3107
+1/1 ━━━━━━━━━━━━━━━━━━━━ 40s 40s/step - FinalLoss: 0.3138
```
+
---
## Perform inference and testing
@@ -935,7 +1204,7 @@ test set. We calculate the Accuracy Score to understand the results closely.
```python
-preds = model.predict(tf.convert_to_tensor(x_test))
+preds = model.predict(ops.convert_to_tensor(x_test))
preds = preds.reshape((preds.shape[0], preds.shape[1]))
@@ -950,11 +1219,13 @@ plt.show()
```
-Test Accuracy score : 97.64%
+Test Accuracy score : 97.56%
```
+
+
---