SQUEEZENET:AlexNet-level Accuracy with 50X fewer parameters and 0.5MB model size 这是由UC Berkerley和Stanford研究人员一起完成的Squeezenet网络结构和设计思想。SqueezeNet设计目标是在保持精度(Alexnet)的情况下简化网络的复杂度。 1、设计原则: 尽量选择1*1卷积核来代替3*3卷积核,因为1*1的卷积核比3*3的卷积...
meshgrid(x, y) u = -1 - X**2 + Y v = 1 + X - Y**2 fig2 = ff.create_streamline(x, y, u, v, arrow_scale=.1, name='Streamline') Edit the figures' x and y axes attributes to create subplots: for i in range(len(fig1.data)): fig1.data[i].xaxis='x1' fig1....
Both x and y-axes are normalized to unity for better comparison. The linear growth and the saturating regions are covered with blue and red shadows respectively. Full size image CLEAR in Applications Above we show that CLEAR is an appropriate FOM to trace the information processing capability of...
facet_row=None, facet_col=None, color_name=None, colormap=None, color_is_cat=False, facet_row_labels=None, facet_col_labels=None, height=None, width=None, trace_type='scatter', scales='fixed', dtick_x=None, dtick_y=None, show_boxes=True, ggplot2=False, binsize=1, **kwargs) ...