[Bugfix][Frontend][Keras]Fix a corner case bug in softmax converter of keras frontend

[jikechao]

This PR fixes a corner bug in the softmax converter in the Keras frontend.
If the input dimension of softmax is 1, TVM will crash unexpectedly.

StackTrace

Traceback (most recent call last):
  File "test.py", line 25, in <module>
    mod, params = relay.frontend.from_keras(model, shape_dict)
  File "/workplace/software/tvm/tvm/python/tvm/relay/frontend/keras.py", line 1565, in from_keras
    _convert_layer(keras_layer, etab)
  File "/workplace/software/tvm/tvm/python/tvm/relay/frontend/keras.py", line 1519, in _convert_layer
    layout,
  File "/workplace/software/tvm/tvm/python/tvm/relay/frontend/keras.py", line 1394, in keras_op_to_relay
    outs = _convert_map[op_name](inexpr, keras_layer, etab, data_layout)
  File "/workplace/software/tvm/tvm/python/tvm/relay/frontend/keras.py", line 134, in _convert_advanced_activation
    dims = len(input_shape)
TypeError: object of type 'NoneType' has no len()

Steps to reproduce

import tvm
import tvm.relay as relay
import numpy as np
from tensorflow import keras
from tensorflow.keras import layers, models

input_shape = (11,)
input_data = np.random.random(size=input_shape)
dtype = 'float32'

x = layers.Input(shape=input_shape[1:], dtype=dtype)

layer = keras.layers.Softmax()
layer.set_weights(layer.get_weights())

y = layer(x)
model = models.Model(x, y)
res_keras = model.predict(input_data)
print(model.summary())

res_keras2 = model(input_data)

shape_dict = {'input_1': input_shape}
mod, params = relay.frontend.from_keras(model, shape_dict)
print(mod)

cc @echuraev @Hzfengsy @leandron

[tvm-bot]

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[echuraev]

LGTM. Thanks!