def depthwise_conv2d(kernel_size, stride=1, activation_fn='linear'): return htfe.siso_tensorflow_eager_module_from_tensorflow_op_fn( lambda: DepthwiseConv2D(kernel_size, strides=stride, padding='same', activation=activation_fn, use_bias=False), {}, name="DepthwiseConv2D_%dx%d" % (kernel_size, kernel_size))
def conv2d(filters, kernel_size, stride=1, activation_fn='linear'): return htfe.siso_tensorflow_eager_module_from_tensorflow_op_fn( lambda: Conv2D(filters, kernel_size, strides=stride, padding='same', activation='relu', use_bias=False), {}, name="Conv2D_%dx%d" % (kernel_size, kernel_size))
def dense(units): return htfe.siso_tensorflow_eager_module_from_tensorflow_op_fn( lambda: Dense(units), {})
def global_average_pooling(): return htfe.siso_tensorflow_eager_module_from_tensorflow_op_fn( GlobalAveragePooling2D, {})
def average_pooling(pool_size, stride=1): return htfe.siso_tensorflow_eager_module_from_tensorflow_op_fn( lambda: AveragePooling2D(pool_size, strides=stride, padding='same'), {}, name="AveragePooling2D_%dx%d" % (pool_size, pool_size))