设置tensorflow序列模型输入层的形状

Question

I'm trying to build a model for multi class classification, but I don't understand how to set the correct input shape. I have a training set with shape (5420, 212) and this is the model I built:

model = models.Sequential()
model.add(layers.Dense(64, activation='relu', input_shape = (5420,)))
model.add(layers.Dense(64, activation='relu'))
model.add(layers.Dense(5, activation='softmax'))
model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])
model.summary()
history = model.fit(X_train, y_train, epochs=20, batch_size=512)

When I run it I get the error:

ValueError: Input 0 of layer sequential_9 is incompatible with the layer: expected axis -1 of input shape to have value 5420 but received input with shape (None, 212)

Why? Isn't the input value correct?

Answer 1

The input shape should be equal to the inputs X 's second dimension, while the output shape should be equal to the outputs Y 's second dimension (assuming that both X and Y are 2-dimensional, ie that they don't have higher dimensions).

import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from sklearn.datasets import make_classification
from sklearn.preprocessing import OneHotEncoder
tf.random.set_seed(0)

# generate some data
X, y = make_classification(n_classes=5, n_samples=5420, n_features=212, n_informative=212, n_redundant=0, random_state=42)
print(X.shape, y.shape)
# (5420, 212) (5420,)

# one-hot encode the target
Y = OneHotEncoder(sparse=False).fit_transform(y.reshape(-1, 1))
print(X.shape, Y.shape)
# (5420, 212) (5420, 5)

# extract the input and output shapes
input_shape = X.shape[1:]  # the input shape is X's second dimension
output_shape = Y.shape[1]  # the output shape is Y's second dimension
print(input_shape, output_shape)
# (212,) 5

# define the model
model = Sequential()
model.add(Dense(64, activation='relu', input_shape=input_shape))
model.add(Dense(64, activation='relu'))
model.add(Dense(output_shape, activation='softmax'))

# compile the model
model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])

# fit the model
history = model.fit(X, Y, epochs=3, batch_size=512)
# Epoch 1/3
# 11/11 [==============================] - 0s 1ms/step - loss: 4.8206 - accuracy: 0.2208
# Epoch 2/3
# 11/11 [==============================] - 0s 1ms/step - loss: 2.8060 - accuracy: 0.3229
# Epoch 3/3
# 11/11 [==============================] - 0s 1ms/step - loss: 2.0705 - accuracy: 0.3989