How does Keras handle numpy variables?

In Keras, it is possible to pass a numpy array directly as input to a model. Keras will automatically convert the numpy array to a tensor and use it as the model’s input.

Here is an example using a numpy array as input:

import numpy as np
from keras.models import Sequential
from keras.layers import Dense

# 创建一个简单的模型
model = Sequential()
model.add(Dense(units=64, activation='relu', input_dim=100))
model.add(Dense(units=10, activation='softmax'))

# 创建一个输入numpy数组
x_train = np.random.random((1000, 100))
y_train = np.random.randint(10, size=(1000,))

# 编译和训练模型
model.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])
model.fit(x_train, y_train, epochs=10, batch_size=32)

In the above example, x_train and y_train are numpy arrays that are passed as the training data and target labels to the fit method.

The model in the above example assumes that the input is a 100-dimensional vector and the output is a classification problem with 10 categories.