SparseTensor support about tfgo HOT 2 OPEN

from tensorflow.keras.layers import Dense, Input
from tensorflow.keras.models import Model
import scipy
import numpy as np

trainX = scipy.sparse.random(1024, 1024)
trainY = np.random.rand(1024, 2)

inputs = Input(shape=(trainX.shape[1],), sparse=True)
outputs = Dense(trainY.shape[1], activation='softmax')(inputs)
model = Model(inputs=inputs, outputs=outputs)
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])

steps = 10
for i in range(steps):
  # For simplicity, we directly use trainX and trainY in this example
  # Usually, this is where batches are prepared
    print(model.train_on_batch(trainX, trainY))
tf.saved_model.save(model, "TFKerasModel/abctest")