Setting the shape of tensorflow sequential model input layer-CodePudding

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?

CodePudding user response：

The input shape should be equal to the length of the input X second dimension, while the output shape should be equal to the length of the output Y second dimension (assuming that both X and Y are 2-dimensional, i.e. 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]   
output_shape = Y.shape[1]   
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