如何使用 tensorflow 数据集训练神经网络？

Question

I am attempting to train a nural network on the emnist dataset but when I attempt to flatten my image, it throws the following error:

WARNING:tensorflow:Model was constructed with shape (None, 28, 28) for input Tensor("flatten_input:0", shape=(None, 28, 28), dtype=float32), but it was called on an input with incompatible shape (None, 1, 28, 28).

I can't figure out what seems to be the problem and have attempted changing my preprocessing, removing batch size from my model.fit and my ds.map.

Here is the full code:

import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
import tensorflow as tf
from tensorflow import keras
import tensorflow_datasets as tfds
import matplotlib.pyplot as plt

def preprocess(dict):
    image = dict['image']
    image = tf.transpose(image)
    label = dict['label']
    return image, label

train_data, validation_data = tfds.load('emnist/letters', split = ['train', 'test'])
train_data_gen = train_data.map(preprocess).shuffle(1000).batch(32)
validation_data_gen = validation_data.map(preprocess).batch(32)

print(train_data_gen)
model = tf.keras.models.Sequential([
    tf.keras.layers.Flatten(input_shape = (28, 28)),
    tf.keras.layers.Dense(128, activation = 'relu'),
    tf.keras.layers.Dropout(0.2),
    tf.keras.layers.Dense(10, activation = 'softmax')
])

model.compile(optimizer = 'adam',
    loss = 'sparse_categorical_crossentropy',
    metrics = ['accuracy'])

early_stopping = keras.callbacks.EarlyStopping(monitor = 'val_accuracy', patience = 10)
history = model.fit(train_data_gen, epochs = 50, batch_size = 32, validation_data = validation_data_gen, callbacks = [early_stopping], verbose = 1)
model.save('emnistmodel.h5')

Answer 1

So there's actually a few things going on here, so let's address them one at a time.

1. Input shape

   So to address your immediate question, you're receiving an incompatible shape error because, well, the shape of the input doesn't match the expected shape.

   In this line tf.keras.layers.Flatten(input_shape=(28, 28)), we are telling the model to expect inputs of shape (28, 28), but this isn't accurate. Our inputs actually have shape (28, 28, 1) because we are taking a 28x28 pixel image with 1 channel (as opposed to a colour image which would have 3 channels r, g, and b). So to solve this immediate problem, we simply update the model to use the shape of the input. ie tf.keras.layers.Flatten(input_shape=(28, 28, 1)),

2. Number of output nodes

   As Rishabh suggested in his answer, the EMNIST dataset has more than 10 balanced classes. However, in your case, you appear to be using EMNIST Letters which has 26 balanced classes. So your neural net should correspondingly have 27 output nodes (since the class labels go from 1.. 26 while our output nodes correspond to 0.. 26) to be able to classify the given data. Of course, giving it extra output nodes will enable it to run as well, but these will give us additional weights to train that are not necessary which will increase the amount of training time needed for our model. In short, your final layer should be tf.keras.layers.Dense(27, activation='softmax')

3. Preprocessing TensorFlow Datasets

   Reading your preprocess() function, I believe you're trying to convert the training and validation datasets into tuples of (image, label). Instead of creating our own function, TensorFlow conveniently implements this for us through the parameter as_supervised .

   Additionally, I see some extra preprocessing that you're trying to achieve such as batching and shuffling the data. Again, TensorFlow implements batch_size and shuffle_files (See common arguments) for us! So loading the dataset would looking something like

   train_data, validation_data = tfds.load('emnist/letters', split=['train', 'test'], shuffle_files=True, batch_size=32, as_supervised=True)

4. Some additional notes

   Also, as a suggestion, consider excluding batch_size from model.fit(). Defining the same thing at two different places is a recipe for bugs and unexpected behaviours. Moreover, when using TensorFlow Datasets, it's not necessary because they already generate batches .

Overall your updated program should look something like this

import matplotlib.pyplot as plt
import tensorflow_datasets as tfds
from tensorflow import keras
import tensorflow as tf
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'


train_data, validation_data = tfds.load('emnist/letters',
                                        split=['train', 'test'],
                                        shuffle_files=True,
                                        batch_size=32,
                                        as_supervised=True)

model = tf.keras.models.Sequential([
    tf.keras.layers.Flatten(input_shape=(28, 28, 1)),
    tf.keras.layers.Dense(128, activation='relu'),
    tf.keras.layers.Dropout(0.2),
    tf.keras.layers.Dense(27, activation='softmax')
])

model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

early_stopping = keras.callbacks.EarlyStopping(
    monitor='val_accuracy', patience=10)

history = model.fit(train_data,
                    epochs=50,
                    validation_data=validation_data,
                    callbacks=[early_stopping],
                    verbose=1)
model.save('emnistmodel.h5')

Hope this helps!

Answer 2

Hie @Rattandeep I just checked the emnist dataset It has 47 different classes and in your dense layer, you have mentioned 10.

If you change your code from

tf.keras.layers.Dense(10, activation = 'softmax')

To this one, it will work

tf.keras.layers.Dense(47, activation = 'softmax')

Thanks