| import os | |
| import numpy as np | |
| import matplotlib.pyplot as plt | |
| %matplotlib inline | |
| import matplotlib as mpl | |
| # plot title | |
| mpl.rcParams['axes.titlesize'] = 18 |
| def stacked_bar_chart(pivoted_df, stack_vals, level_values_field, chart_title, x_label, y_label, filename, color1, color2): | |
| # | |
| # stacked_bar_chart: draws and saves a barchart figure to filename | |
| # | |
| # pivoted_df: dataframe which has been pivoted so columns correspond to the values to be plotted | |
| # stack_vals: the column names in pivoted_df to plot | |
| # level_values_field: column in the dataframe which has the values to be plotted along the x axis (typically time dimension) | |
| # chart_title: how to title chart | |
| # x_label: label for x axis | |
| # y_label: label for y axis |
| from math import sqrt | |
| def put_kernels_on_grid (kernel, pad = 1): | |
| '''Visualize conv. filters as an image (mostly for the 1st layer). | |
| Arranges filters into a grid, with some paddings between adjacent filters. | |
| Args: | |
| kernel: tensor of shape [Y, X, NumChannels, NumKernels] | |
| pad: number of black pixels around each filter (between them) |
##VGG16 model for Keras
This is the Keras model of the 16-layer network used by the VGG team in the ILSVRC-2014 competition.
It has been obtained by directly converting the Caffe model provived by the authors.
Details about the network architecture can be found in the following arXiv paper:
Very Deep Convolutional Networks for Large-Scale Image Recognition
K. Simonyan, A. Zisserman
| """ | |
| Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy) | |
| BSD License | |
| """ | |
| import numpy as np | |
| # data I/O | |
| data = open('input.txt', 'r').read() # should be simple plain text file | |
| chars = list(set(data)) | |
| data_size, vocab_size = len(data), len(chars) |