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Keras debugging tips

Author: fchollet
Date created: 2020/05/16
Last modified: 2023/11/16
Description: Four simple tips to help you debug your Keras code.

View in Colab • GitHub source

Introduction

It's generally possible to do almost anything in Keras without writing code per se: whether you're implementing a new type of GAN or the latest convnet architecture for image segmentation, you can usually stick to calling built-in methods. Because all built-in methods do extensive input validation checks, you will have little to no debugging to do. A Functional API model made entirely of built-in layers will work on first try -- if you can compile it, it will run.

However, sometimes, you will need to dive deeper and write your own code. Here are some common examples:

Creating a new Layer subclass.
Creating a custom Metric subclass.
Implementing a custom train_step on a Model.

This document provides a few simple tips to help you navigate debugging in these situations.

Tip 1: test each part before you test the whole

If you've created any object that has a chance of not working as expected, don't just drop it in your end-to-end process and watch sparks fly. Rather, test your custom object in isolation first. This may seem obvious -- but you'd be surprised how often people don't start with this.

If you write a custom layer, don't call fit() on your entire model just yet. Call your layer on some test data first.
If you write a custom metric, start by printing its output for some reference inputs.

Here's a simple example. Let's write a custom layer a bug in it:

import os

# The last example uses tf.GradientTape and thus requires TensorFlow.
# However, all tips here are applicable with all backends.
os.environ["KERAS_BACKEND"] = "tensorflow"

import keras
from keras import layers
from keras import ops
import numpy as np
import tensorflow as tf


class MyAntirectifier(layers.Layer):
    def build(self, input_shape):
        output_dim = input_shape[-1]
        self.kernel = self.add_weight(
            shape=(output_dim * 2, output_dim),
            initializer="he_normal",
            name="kernel",
            trainable=True,
        )

    def call(self, inputs):
        # Take the positive part of the input
        pos = ops.relu(inputs)
        # Take the negative part of the input
        neg = ops.relu(-inputs)
        # Concatenate the positive and negative parts
        concatenated = ops.concatenate([pos, neg], axis=0)
        # Project the concatenation down to the same dimensionality as the input
        return ops.matmul(concatenated, self.kernel)

Now, rather than using it in a end-to-end model directly, let's try to call the layer on some test data:

x = tf.random.normal(shape=(2, 5))
y = MyAntirectifier()(x)

We get the following error:

...
      1 x = tf.random.normal(shape=(2, 5))
----> 2 y = MyAntirectifier()(x)
...
     17         neg = tf.nn.relu(-inputs)
     18         concatenated = tf.concat([pos, neg], axis=0)
---> 19         return tf.matmul(concatenated, self.kernel)
...
InvalidArgumentError: Matrix size-incompatible: In[0]: [4,5], In[1]: [10,5] [Op:MatMul]

Looks like our input tensor in the matmul op may have an incorrect shape. Let's add a print statement to check the actual shapes:


class MyAntirectifier(layers.Layer):
    def build(self, input_shape):
        output_dim = input_shape[-1]
        self.kernel = self.add_weight(
            shape=(output_dim * 2, output_dim),
            initializer="he_normal",
            name="kernel",
            trainable=True,
        )

    def call(self, inputs):
        pos = ops.relu(inputs)
        neg = ops.relu(-inputs)
        print("pos.shape:", pos.shape)
        print("neg.shape:", neg.shape)
        concatenated = ops.concatenate([pos, neg], axis=0)
        print("concatenated.shape:", concatenated.shape)
        print("kernel.shape:", self.kernel.shape)
        return ops.matmul(concatenated, self.kernel)

We get the following:

pos.shape: (2, 5)
neg.shape: (2, 5)
concatenated.shape: (4, 5)
kernel.shape: (10, 5)

Turns out we had the wrong axis for the concat op! We should be concatenating neg and pos alongside the feature axis 1, not the batch axis 0. Here's the correct version:


class MyAntirectifier(layers.Layer):
    def build(self, input_shape):
        output_dim = input_shape[-1]
        self.kernel = self.add_weight(
            shape=(output_dim * 2, output_dim),
            initializer="he_normal",
            name="kernel",
            trainable=True,
        )

    def call(self, inputs):
        pos = ops.relu(inputs)
        neg = ops.relu(-inputs)
        print("pos.shape:", pos.shape)
        print("neg.shape:", neg.shape)
        concatenated = ops.concatenate([pos, neg], axis=1)
        print("concatenated.shape:", concatenated.shape)
        print("kernel.shape:", self.kernel.shape)
        return ops.matmul(concatenated, self.kernel)

Now our code works fine:

x = keras.random.normal(shape=(2, 5))
y = MyAntirectifier()(x)

``` pos.shape: (2, 5) neg.shape: (2, 5) concatenated.shape: (2, 10) kernel.shape: (10, 5)

</div>
---
## Tip 2: use `model.summary()` and `plot_model()` to check layer output shapes

If you're working with complex network topologies, you're going to need a way
to visualize how your layers are connected and how they transform the data that passes
through them.

Here's an example. Consider this model with three inputs and two outputs (lifted from the
[Functional API guide](https://keras.io/guides/functional_api/#manipulate-complex-graph-topologies)):


```python
num_tags = 12  # Number of unique issue tags
num_words = 10000  # Size of vocabulary obtained when preprocessing text data
num_departments = 4  # Number of departments for predictions

title_input = keras.Input(
    shape=(None,), name="title"
)  # Variable-length sequence of ints
body_input = keras.Input(shape=(None,), name="body")  # Variable-length sequence of ints
tags_input = keras.Input(
    shape=(num_tags,), name="tags"
)  # Binary vectors of size `num_tags`

# Embed each word in the title into a 64-dimensional vector
title_features = layers.Embedding(num_words, 64)(title_input)
# Embed each word in the text into a 64-dimensional vector
body_features = layers.Embedding(num_words, 64)(body_input)

# Reduce sequence of embedded words in the title into a single 128-dimensional vector
title_features = layers.LSTM(128)(title_features)
# Reduce sequence of embedded words in the body into a single 32-dimensional vector
body_features = layers.LSTM(32)(body_features)

# Merge all available features into a single large vector via concatenation
x = layers.concatenate([title_features, body_features, tags_input])

# Stick a logistic regression for priority prediction on top of the features
priority_pred = layers.Dense(1, name="priority")(x)
# Stick a department classifier on top of the features
department_pred = layers.Dense(num_departments, name="department")(x)

# Instantiate an end-to-end model predicting both priority and department
model = keras.Model(
    inputs=[title_input, body_input, tags_input],
    outputs=[priority_pred, department_pred],
)

Calling summary() can help you check the output shape of each layer:

model.summary()

Model: "functional_1"

┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━┓
┃ Layer (type)        ┃ Output Shape      ┃ Param # ┃ Connected to         ┃
┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━┩
│ title (InputLayer)  │ (None, None)      │       0 │ -                    │
├─────────────────────┼───────────────────┼─────────┼──────────────────────┤
│ body (InputLayer)   │ (None, None)      │       0 │ -                    │
├─────────────────────┼───────────────────┼─────────┼──────────────────────┤
│ embedding           │ (None, None, 64)  │ 640,000 │ title[0][0]          │
│ (Embedding)         │                   │         │                      │
├─────────────────────┼───────────────────┼─────────┼──────────────────────┤
│ embedding_1         │ (None, None, 64)  │ 640,000 │ body[0][0]           │
│ (Embedding)         │                   │         │                      │
├─────────────────────┼───────────────────┼─────────┼──────────────────────┤
│ lstm (LSTM)         │ (None, 128)       │  98,816 │ embedding[0][0]      │
├─────────────────────┼───────────────────┼─────────┼──────────────────────┤
│ lstm_1 (LSTM)       │ (None, 32)        │  12,416 │ embedding_1[0][0]    │
├─────────────────────┼───────────────────┼─────────┼──────────────────────┤
│ tags (InputLayer)   │ (None, 12)        │       0 │ -                    │
├─────────────────────┼───────────────────┼─────────┼──────────────────────┤
│ concatenate         │ (None, 172)       │       0 │ lstm[0][0],          │
│ (Concatenate)       │                   │         │ lstm_1[0][0],        │
│                     │                   │         │ tags[0][0]           │
├─────────────────────┼───────────────────┼─────────┼──────────────────────┤
│ priority (Dense)    │ (None, 1)         │     173 │ concatenate[0][0]    │
├─────────────────────┼───────────────────┼─────────┼──────────────────────┤
│ department (Dense)  │ (None, 4)         │     692 │ concatenate[0][0]    │
└─────────────────────┴───────────────────┴─────────┴──────────────────────┘

 Total params: 1,392,097 (5.31 MB)

 Trainable params: 1,392,097 (5.31 MB)

 Non-trainable params: 0 (0.00 B)

You can also visualize the entire network topology alongside output shapes using plot_model:

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

With this plot, any connectivity-level error becomes immediately obvious.

Tip 3: to debug what happens during `fit()`, use `run_eagerly=True`

The fit() method is fast: it runs a well-optimized, fully-compiled computation graph. That's great for performance, but it also means that the code you're executing isn't the Python code you've written. This can be problematic when debugging. As you may recall, Python is slow -- so we use it as a staging language, not as an execution language.

Thankfully, there's an easy way to run your code in "debug mode", fully eagerly: pass run_eagerly=True to compile(). Your call to fit() will now get executed line by line, without any optimization. It's slower, but it makes it possible to print the value of intermediate tensors, or to use a Python debugger. Great for debugging.

Here's a basic example: let's write a really simple model with a custom train_step() method. Our model just implements gradient descent, but instead of first-order gradients, it uses a combination of first-order and second-order gradients. Pretty simple so far.

Can you spot what we're doing wrong?


class MyModel(keras.Model):
    def train_step(self, data):
        inputs, targets = data
        trainable_vars = self.trainable_variables
        with tf.GradientTape() as tape2:
            with tf.GradientTape() as tape1:
                y_pred = self(inputs, training=True)  # Forward pass
                # Compute the loss value
                # (the loss function is configured in `compile()`)
                loss = self.compute_loss(y=targets, y_pred=y_pred)
            # Compute first-order gradients
            dl_dw = tape1.gradient(loss, trainable_vars)
        # Compute second-order gradients
        d2l_dw2 = tape2.gradient(dl_dw, trainable_vars)

        # Combine first-order and second-order gradients
        grads = [0.5 * w1 + 0.5 * w2 for (w1, w2) in zip(d2l_dw2, dl_dw)]

        # Update weights
        self.optimizer.apply_gradients(zip(grads, trainable_vars))

        # Update metrics (includes the metric that tracks the loss)
        for metric in self.metrics:
            if metric.name == "loss":
                metric.update_state(loss)
            else:
                metric.update_state(targets, y_pred)

        # Return a dict mapping metric names to current value
        return {m.name: m.result() for m in self.metrics}

Let's train a one-layer model on MNIST with this custom loss function.

We pick, somewhat at random, a batch size of 1024 and a learning rate of 0.1. The general idea being to use larger batches and a larger learning rate than usual, since our "improved" gradients should lead us to quicker convergence.


# Construct an instance of MyModel
def get_model():
    inputs = keras.Input(shape=(784,))
    intermediate = layers.Dense(256, activation="relu")(inputs)
    outputs = layers.Dense(10, activation="softmax")(intermediate)
    model = MyModel(inputs, outputs)
    return model


# Prepare data
(x_train, y_train), _ = keras.datasets.mnist.load_data()
x_train = np.reshape(x_train, (-1, 784)) / 255

model = get_model()
model.compile(
    optimizer=keras.optimizers.SGD(learning_rate=1e-2),
    loss="sparse_categorical_crossentropy",
)
model.fit(x_train, y_train, epochs=3, batch_size=1024, validation_split=0.1)

``` Epoch 1/3 53/53 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - loss: 2.4264 - val_loss: 2.3036 Epoch 2/3 53/53 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - loss: 2.3111 - val_loss: 2.3387 Epoch 3/3 53/53 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - loss: 2.3442 - val_loss: 2.3697

<keras.src.callbacks.history.History at 0x29a899600>

</div>
Oh no, it doesn't converge! Something is not working as planned.

Time for some step-by-step printing of what's going on with our gradients.

We add various `print` statements in the `train_step` method, and we make sure to pass
`run_eagerly=True` to `compile()` to run our code step-by-step, eagerly.


```python

class MyModel(keras.Model):
    def train_step(self, data):
        print()
        print("----Start of step: %d" % (self.step_counter,))
        self.step_counter += 1

        inputs, targets = data
        trainable_vars = self.trainable_variables
        with tf.GradientTape() as tape2:
            with tf.GradientTape() as tape1:
                y_pred = self(inputs, training=True)  # Forward pass
                # Compute the loss value
                # (the loss function is configured in `compile()`)
                loss = self.compute_loss(y=targets, y_pred=y_pred)
            # Compute first-order gradients
            dl_dw = tape1.gradient(loss, trainable_vars)
        # Compute second-order gradients
        d2l_dw2 = tape2.gradient(dl_dw, trainable_vars)

        print("Max of dl_dw[0]: %.4f" % tf.reduce_max(dl_dw[0]))
        print("Min of dl_dw[0]: %.4f" % tf.reduce_min(dl_dw[0]))
        print("Mean of dl_dw[0]: %.4f" % tf.reduce_mean(dl_dw[0]))
        print("-")
        print("Max of d2l_dw2[0]: %.4f" % tf.reduce_max(d2l_dw2[0]))
        print("Min of d2l_dw2[0]: %.4f" % tf.reduce_min(d2l_dw2[0]))
        print("Mean of d2l_dw2[0]: %.4f" % tf.reduce_mean(d2l_dw2[0]))

        # Combine first-order and second-order gradients
        grads = [0.5 * w1 + 0.5 * w2 for (w1, w2) in zip(d2l_dw2, dl_dw)]

        # Update weights
        self.optimizer.apply_gradients(zip(grads, trainable_vars))

        # Update metrics (includes the metric that tracks the loss)
        for metric in self.metrics:
            if metric.name == "loss":
                metric.update_state(loss)
            else:
                metric.update_state(targets, y_pred)

        # Return a dict mapping metric names to current value
        return {m.name: m.result() for m in self.metrics}


model = get_model()
model.compile(
    optimizer=keras.optimizers.SGD(learning_rate=1e-2),
    loss="sparse_categorical_crossentropy",
    metrics=["sparse_categorical_accuracy"],
    run_eagerly=True,
)
model.step_counter = 0
# We pass epochs=1 and steps_per_epoch=10 to only run 10 steps of training.
model.fit(x_train, y_train, epochs=1, batch_size=1024, verbose=0, steps_per_epoch=10)

``` ----Start of step: 0 Max of dl_dw[0]: 0.0332 Min of dl_dw[0]: -0.0288 Mean of dl_dw[0]: 0.0003 - Max of d2l_dw2[0]: 5.2691 Min of d2l_dw2[0]: -2.6968 Mean of d2l_dw2[0]: 0.0981 ```

``` ----Start of step: 1 Max of dl_dw[0]: 0.0445 Min of dl_dw[0]: -0.0169 Mean of dl_dw[0]: 0.0013 - Max of d2l_dw2[0]: 3.3575 Min of d2l_dw2[0]: -1.9024 Mean of d2l_dw2[0]: 0.0726 ```

``` ----Start of step: 2 Max of dl_dw[0]: 0.0669 Min of dl_dw[0]: -0.0153 Mean of dl_dw[0]: 0.0013 - Max of d2l_dw2[0]: 5.0661 Min of d2l_dw2[0]: -1.7168 Mean of d2l_dw2[0]: 0.0809 ```

``` ----Start of step: 3 Max of dl_dw[0]: 0.0545 Min of dl_dw[0]: -0.0125 Mean of dl_dw[0]: 0.0008 - Max of d2l_dw2[0]: 6.5223 Min of d2l_dw2[0]: -0.6604 Mean of d2l_dw2[0]: 0.0991 ```

``` ----Start of step: 4 Max of dl_dw[0]: 0.0247 Min of dl_dw[0]: -0.0152 Mean of dl_dw[0]: -0.0001 - Max of d2l_dw2[0]: 2.8030 Min of d2l_dw2[0]: -0.1156 Mean of d2l_dw2[0]: 0.0321 ```

``` ----Start of step: 5 Max of dl_dw[0]: 0.0051 Min of dl_dw[0]: -0.0096 Mean of dl_dw[0]: -0.0001 - Max of d2l_dw2[0]: 0.2545 Min of d2l_dw2[0]: -0.0284 Mean of d2l_dw2[0]: 0.0079 ```

``` ----Start of step: 6 Max of dl_dw[0]: 0.0041 Min of dl_dw[0]: -0.0102 Mean of dl_dw[0]: -0.0001 - Max of d2l_dw2[0]: 0.2198 Min of d2l_dw2[0]: -0.0175 Mean of d2l_dw2[0]: 0.0069 ```

``` ----Start of step: 7 Max of dl_dw[0]: 0.0035 Min of dl_dw[0]: -0.0086 Mean of dl_dw[0]: -0.0001 - Max of d2l_dw2[0]: 0.1485 Min of d2l_dw2[0]: -0.0175 Mean of d2l_dw2[0]: 0.0060 ```

``` ----Start of step: 8 Max of dl_dw[0]: 0.0039 Min of dl_dw[0]: -0.0094 Mean of dl_dw[0]: -0.0001 - Max of d2l_dw2[0]: 0.1454 Min of d2l_dw2[0]: -0.0130 Mean of d2l_dw2[0]: 0.0061 ```

``` ----Start of step: 9 Max of dl_dw[0]: 0.0028 Min of dl_dw[0]: -0.0087 Mean of dl_dw[0]: -0.0001 - Max of d2l_dw2[0]: 0.1491 Min of d2l_dw2[0]: -0.0326 Mean of d2l_dw2[0]: 0.0058

<keras.src.callbacks.history.History at 0x2a0d1e440>

</div>
What did we learn?

- The first order and second order gradients can have values that differ by orders of
magnitudes.
- Sometimes, they may not even have the same sign.
- Their values can vary greatly at each step.

This leads us to an obvious idea: let's normalize the gradients before combining them.


```python

class MyModel(keras.Model):
    def train_step(self, data):
        inputs, targets = data
        trainable_vars = self.trainable_variables
        with tf.GradientTape() as tape2:
            with tf.GradientTape() as tape1:
                y_pred = self(inputs, training=True)  # Forward pass
                # Compute the loss value
                # (the loss function is configured in `compile()`)
                loss = self.compute_loss(y=targets, y_pred=y_pred)
            # Compute first-order gradients
            dl_dw = tape1.gradient(loss, trainable_vars)
        # Compute second-order gradients
        d2l_dw2 = tape2.gradient(dl_dw, trainable_vars)

        dl_dw = [tf.math.l2_normalize(w) for w in dl_dw]
        d2l_dw2 = [tf.math.l2_normalize(w) for w in d2l_dw2]

        # Combine first-order and second-order gradients
        grads = [0.5 * w1 + 0.5 * w2 for (w1, w2) in zip(d2l_dw2, dl_dw)]

        # Update weights
        self.optimizer.apply_gradients(zip(grads, trainable_vars))

        # Update metrics (includes the metric that tracks the loss)
        for metric in self.metrics:
            if metric.name == "loss":
                metric.update_state(loss)
            else:
                metric.update_state(targets, y_pred)

        # Return a dict mapping metric names to current value
        return {m.name: m.result() for m in self.metrics}


model = get_model()
model.compile(
    optimizer=keras.optimizers.SGD(learning_rate=1e-2),
    loss="sparse_categorical_crossentropy",
    metrics=["sparse_categorical_accuracy"],
)
model.fit(x_train, y_train, epochs=5, batch_size=1024, validation_split=0.1)

``` Epoch 1/5 53/53 ━━━━━━━━━━━━━━━━━━━━ 1s 7ms/step - sparse_categorical_accuracy: 0.1250 - loss: 2.3185 - val_loss: 2.0502 - val_sparse_categorical_accuracy: 0.3373 Epoch 2/5 53/53 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - sparse_categorical_accuracy: 0.3966 - loss: 1.9934 - val_loss: 1.8032 - val_sparse_categorical_accuracy: 0.5698 Epoch 3/5 53/53 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - sparse_categorical_accuracy: 0.5663 - loss: 1.7784 - val_loss: 1.6241 - val_sparse_categorical_accuracy: 0.6470 Epoch 4/5 53/53 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - sparse_categorical_accuracy: 0.6135 - loss: 1.6256 - val_loss: 1.5010 - val_sparse_categorical_accuracy: 0.6595 Epoch 5/5 53/53 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - sparse_categorical_accuracy: 0.6216 - loss: 1.5173 - val_loss: 1.4169 - val_sparse_categorical_accuracy: 0.6625

<keras.src.callbacks.history.History at 0x2a0d4c640>

</div>
Now, training converges! It doesn't work well at all, but at least the model learns
something.

After spending a few minutes tuning parameters, we get to the following configuration
that works somewhat well (achieves 97% validation accuracy and seems reasonably robust to
overfitting):

- Use `0.2 * w1 + 0.8 * w2` for combining gradients.
- Use a learning rate that decays linearly over time.

I'm not going to say that the idea works -- this isn't at all how you're supposed to do
second-order optimization (pointers: see the Newton & Gauss-Newton methods, quasi-Newton
methods, and BFGS). But hopefully this demonstration gave you an idea of how you can
debug your way out of uncomfortable training situations.

Remember: use `run_eagerly=True` for debugging what happens in `fit()`. And when your code
is finally working as expected, make sure to remove this flag in order to get the best
runtime performance!

Here's our final training run:


```python

class MyModel(keras.Model):
    def train_step(self, data):
        inputs, targets = data
        trainable_vars = self.trainable_variables
        with tf.GradientTape() as tape2:
            with tf.GradientTape() as tape1:
                y_pred = self(inputs, training=True)  # Forward pass
                # Compute the loss value
                # (the loss function is configured in `compile()`)
                loss = self.compute_loss(y=targets, y_pred=y_pred)
            # Compute first-order gradients
            dl_dw = tape1.gradient(loss, trainable_vars)
        # Compute second-order gradients
        d2l_dw2 = tape2.gradient(dl_dw, trainable_vars)

        dl_dw = [tf.math.l2_normalize(w) for w in dl_dw]
        d2l_dw2 = [tf.math.l2_normalize(w) for w in d2l_dw2]

        # Combine first-order and second-order gradients
        grads = [0.2 * w1 + 0.8 * w2 for (w1, w2) in zip(d2l_dw2, dl_dw)]

        # Update weights
        self.optimizer.apply_gradients(zip(grads, trainable_vars))

        # Update metrics (includes the metric that tracks the loss)
        for metric in self.metrics:
            if metric.name == "loss":
                metric.update_state(loss)
            else:
                metric.update_state(targets, y_pred)

        # Return a dict mapping metric names to current value
        return {m.name: m.result() for m in self.metrics}


model = get_model()
lr = learning_rate = keras.optimizers.schedules.InverseTimeDecay(
    initial_learning_rate=0.1, decay_steps=25, decay_rate=0.1
)
model.compile(
    optimizer=keras.optimizers.SGD(lr),
    loss="sparse_categorical_crossentropy",
    metrics=["sparse_categorical_accuracy"],
)
model.fit(x_train, y_train, epochs=50, batch_size=2048, validation_split=0.1)

``` Epoch 1/50 27/27 ━━━━━━━━━━━━━━━━━━━━ 1s 14ms/step - sparse_categorical_accuracy: 0.5056 - loss: 1.7508 - val_loss: 0.6378 - val_sparse_categorical_accuracy: 0.8658 Epoch 2/50 27/27 ━━━━━━━━━━━━━━━━━━━━ 0s 10ms/step - sparse_categorical_accuracy: 0.8407 - loss: 0.6323 - val_loss: 0.4039 - val_sparse_categorical_accuracy: 0.8970 Epoch 3/50 27/27 ━━━━━━━━━━━━━━━━━━━━ 0s 10ms/step - sparse_categorical_accuracy: 0.8807 - loss: 0.4472 - val_loss: 0.3243 - val_sparse_categorical_accuracy: 0.9120 Epoch 4/50 27/27 ━━━━━━━━━━━━━━━━━━━━ 0s 10ms/step - sparse_categorical_accuracy: 0.8947 - loss: 0.3781 - val_loss: 0.2861 - val_sparse_categorical_accuracy: 0.9235 Epoch 5/50 27/27 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - sparse_categorical_accuracy: 0.9022 - loss: 0.3453 - val_loss: 0.2622 - val_sparse_categorical_accuracy: 0.9288 Epoch 6/50 27/27 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - sparse_categorical_accuracy: 0.9093 - loss: 0.3243 - val_loss: 0.2523 - val_sparse_categorical_accuracy: 0.9303 Epoch 7/50 27/27 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - sparse_categorical_accuracy: 0.9148 - loss: 0.3021 - val_loss: 0.2362 - val_sparse_categorical_accuracy: 0.9338 Epoch 8/50 27/27 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - sparse_categorical_accuracy: 0.9184 - loss: 0.2899 - val_loss: 0.2289 - val_sparse_categorical_accuracy: 0.9365 Epoch 9/50 27/27 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - sparse_categorical_accuracy: 0.9212 - loss: 0.2784 - val_loss: 0.2183 - val_sparse_categorical_accuracy: 0.9383 Epoch 10/50 27/27 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - sparse_categorical_accuracy: 0.9246 - loss: 0.2670 - val_loss: 0.2097 - val_sparse_categorical_accuracy: 0.9405 Epoch 11/50 27/27 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - sparse_categorical_accuracy: 0.9267 - loss: 0.2563 - val_loss: 0.2063 - val_sparse_categorical_accuracy: 0.9442 Epoch 12/50 27/27 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - sparse_categorical_accuracy: 0.9313 - loss: 0.2412 - val_loss: 0.1965 - val_sparse_categorical_accuracy: 0.9458 Epoch 13/50 27/27 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - sparse_categorical_accuracy: 0.9324 - loss: 0.2411 - val_loss: 0.1917 - val_sparse_categorical_accuracy: 0.9472 Epoch 14/50 27/27 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - sparse_categorical_accuracy: 0.9359 - loss: 0.2260 - val_loss: 0.1861 - val_sparse_categorical_accuracy: 0.9495 Epoch 15/50 27/27 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - sparse_categorical_accuracy: 0.9374 - loss: 0.2234 - val_loss: 0.1804 - val_sparse_categorical_accuracy: 0.9517 Epoch 16/50 27/27 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step - sparse_categorical_accuracy: 0.9382 - loss: 0.2196 - val_loss: 0.1761 - val_sparse_categorical_accuracy: 0.9528 Epoch 17/50 27/27 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step - sparse_categorical_accuracy: 0.9417 - loss: 0.2076 - val_loss: 0.1709 - val_sparse_categorical_accuracy: 0.9557 Epoch 18/50 27/27 ━━━━━━━━━━━━━━━━━━━━ 0s 13ms/step - sparse_categorical_accuracy: 0.9423 - loss: 0.2032 - val_loss: 0.1664 - val_sparse_categorical_accuracy: 0.9555 Epoch 19/50 27/27 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - sparse_categorical_accuracy: 0.9444 - loss: 0.1953 - val_loss: 0.1616 - val_sparse_categorical_accuracy: 0.9582 Epoch 20/50 27/27 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - sparse_categorical_accuracy: 0.9451 - loss: 0.1916 - val_loss: 0.1597 - val_sparse_categorical_accuracy: 0.9592 Epoch 21/50 27/27 ━━━━━━━━━━━━━━━━━━━━ 0s 13ms/step - sparse_categorical_accuracy: 0.9473 - loss: 0.1866 - val_loss: 0.1563 - val_sparse_categorical_accuracy: 0.9615 Epoch 22/50 27/27 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - sparse_categorical_accuracy: 0.9486 - loss: 0.1818 - val_loss: 0.1520 - val_sparse_categorical_accuracy: 0.9617 Epoch 23/50 27/27 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - sparse_categorical_accuracy: 0.9502 - loss: 0.1794 - val_loss: 0.1499 - val_sparse_categorical_accuracy: 0.9635 Epoch 24/50 27/27 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - sparse_categorical_accuracy: 0.9502 - loss: 0.1759 - val_loss: 0.1466 - val_sparse_categorical_accuracy: 0.9640 Epoch 25/50 27/27 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - sparse_categorical_accuracy: 0.9515 - loss: 0.1714 - val_loss: 0.1437 - val_sparse_categorical_accuracy: 0.9645 Epoch 26/50 27/27 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step - sparse_categorical_accuracy: 0.9535 - loss: 0.1649 - val_loss: 0.1435 - val_sparse_categorical_accuracy: 0.9640 Epoch 27/50 27/27 ━━━━━━━━━━━━━━━━━━━━ 0s 13ms/step - sparse_categorical_accuracy: 0.9548 - loss: 0.1628 - val_loss: 0.1411 - val_sparse_categorical_accuracy: 0.9650 Epoch 28/50 27/27 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - sparse_categorical_accuracy: 0.9541 - loss: 0.1620 - val_loss: 0.1384 - val_sparse_categorical_accuracy: 0.9655 Epoch 29/50 27/27 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - sparse_categorical_accuracy: 0.9564 - loss: 0.1560 - val_loss: 0.1359 - val_sparse_categorical_accuracy: 0.9668 Epoch 30/50 27/27 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - sparse_categorical_accuracy: 0.9577 - loss: 0.1547 - val_loss: 0.1338 - val_sparse_categorical_accuracy: 0.9672 Epoch 31/50 27/27 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - sparse_categorical_accuracy: 0.9569 - loss: 0.1520 - val_loss: 0.1329 - val_sparse_categorical_accuracy: 0.9663 Epoch 32/50 27/27 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - sparse_categorical_accuracy: 0.9582 - loss: 0.1478 - val_loss: 0.1320 - val_sparse_categorical_accuracy: 0.9675 Epoch 33/50 27/27 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - sparse_categorical_accuracy: 0.9582 - loss: 0.1483 - val_loss: 0.1292 - val_sparse_categorical_accuracy: 0.9670 Epoch 34/50 27/27 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - sparse_categorical_accuracy: 0.9594 - loss: 0.1448 - val_loss: 0.1274 - val_sparse_categorical_accuracy: 0.9677 Epoch 35/50 27/27 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - sparse_categorical_accuracy: 0.9587 - loss: 0.1452 - val_loss: 0.1262 - val_sparse_categorical_accuracy: 0.9678 Epoch 36/50 27/27 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - sparse_categorical_accuracy: 0.9603 - loss: 0.1418 - val_loss: 0.1251 - val_sparse_categorical_accuracy: 0.9677 Epoch 37/50 27/27 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - sparse_categorical_accuracy: 0.9603 - loss: 0.1402 - val_loss: 0.1238 - val_sparse_categorical_accuracy: 0.9682 Epoch 38/50 27/27 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - sparse_categorical_accuracy: 0.9618 - loss: 0.1382 - val_loss: 0.1228 - val_sparse_categorical_accuracy: 0.9680 Epoch 39/50 27/27 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - sparse_categorical_accuracy: 0.9630 - loss: 0.1335 - val_loss: 0.1213 - val_sparse_categorical_accuracy: 0.9695 Epoch 40/50 27/27 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - sparse_categorical_accuracy: 0.9629 - loss: 0.1327 - val_loss: 0.1198 - val_sparse_categorical_accuracy: 0.9698 Epoch 41/50 27/27 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - sparse_categorical_accuracy: 0.9639 - loss: 0.1323 - val_loss: 0.1191 - val_sparse_categorical_accuracy: 0.9695 Epoch 42/50 27/27 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - sparse_categorical_accuracy: 0.9629 - loss: 0.1346 - val_loss: 0.1183 - val_sparse_categorical_accuracy: 0.9692 Epoch 43/50 27/27 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - sparse_categorical_accuracy: 0.9661 - loss: 0.1262 - val_loss: 0.1182 - val_sparse_categorical_accuracy: 0.9700 Epoch 44/50 27/27 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - sparse_categorical_accuracy: 0.9652 - loss: 0.1274 - val_loss: 0.1163 - val_sparse_categorical_accuracy: 0.9702 Epoch 45/50 27/27 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - sparse_categorical_accuracy: 0.9650 - loss: 0.1259 - val_loss: 0.1154 - val_sparse_categorical_accuracy: 0.9708 Epoch 46/50 27/27 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - sparse_categorical_accuracy: 0.9647 - loss: 0.1246 - val_loss: 0.1148 - val_sparse_categorical_accuracy: 0.9703 Epoch 47/50 27/27 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - sparse_categorical_accuracy: 0.9659 - loss: 0.1236 - val_loss: 0.1137 - val_sparse_categorical_accuracy: 0.9707 Epoch 48/50 27/27 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - sparse_categorical_accuracy: 0.9665 - loss: 0.1221 - val_loss: 0.1133 - val_sparse_categorical_accuracy: 0.9710 Epoch 49/50 27/27 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - sparse_categorical_accuracy: 0.9675 - loss: 0.1192 - val_loss: 0.1124 - val_sparse_categorical_accuracy: 0.9712 Epoch 50/50 27/27 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - sparse_categorical_accuracy: 0.9664 - loss: 0.1214 - val_loss: 0.1112 - val_sparse_categorical_accuracy: 0.9707

<keras.src.callbacks.history.History at 0x29e76ae60>

</div>

Keras debugging tips

Introduction

Tip 1: test each part before you test the whole

Tip 3: to debug what happens during `fit()`, use `run_eagerly=True`

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Keras debugging tips

Introduction

Tip 1: test each part before you test the whole

Tip 3: to debug what happens during fit(), use run_eagerly=True

Tip 3: to debug what happens during `fit()`, use `run_eagerly=True`