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How to download from pan.baidu.com

Disclaimer:

• This methods uses a 3rd party website: https://baidu.kinh.cc/.
• I don't know chinese and after one day of searching for a method I finally found this. I don't know how safe this website is but it does the job.
• Do it on your own responsibility. I have no idea about possible copyright (if there is such a thing in China) and other stuff regarding to this.

Steps

1. Open the website mentioned above and fill out fields as following:

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#!/usr/bin/env python
from __future__ import print_function
import argparse
import numpy as np
import time
tt = time.time()

import cv2
from grpc.beta import implementations

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# -*- coding: utf-8 -*-
"""
Created on Mon Jun 29 14:00:37 2015

@author: alexandrebarachant
"""

import numpy as np
import pandas as pd
from mne.io import RawArray

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# -*- coding: utf-8 -*-
"""
Created on Fri May 05 04:12:36 2017

@author: ADubey4
"""

from __future__ import unicode_literals, print_function
import gensim
from gensim.parsing import PorterStemmer

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import tensorflow as tf

# The export path contains the name and the version of the model
tf.keras.backend.set_learning_phase(0)  # Ignore dropout at inference
model = tf.keras.models.load_model('./inception.h5')
export_path = '../my_image_classifier/1'

# Fetch the Keras session and save the model
# The signature definition is defined by the input and output tensors
# And stored with the default serving key

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from keras import backend as K
from keras import regularizers, constraints, initializers, activations
from keras.layers.recurrent import RNN, Layer, _generate_dropout_mask, _generate_dropout_ones
from keras.engine import InputSpec
from keras.legacy import interfaces
import warnings


# Copied from original keras source
def _time_distributed_dense(x, w, b=None, dropout=None,

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Building custom layers in Keras

About Keras

Keras is currently one of the most commonly used deep learning libraries today. And part of the reason why it's so popular is its API. Keras was built as a high-level API for other deep learning libraries ie Keras as such does not perform low-level tensor operations, instead provides an interface to its backend which are built for such operations. This allows Keras to abstract a lot of the underlying details and allows the programmer to concentrate on the architecture of the model. Currently Keras supports Tensorflow, Theano and CNTK as its backends.

Let's see what I mean. Tensorflow is one of the backends used by Keras. Here's the code for MNIST classification in TensorFlow and Keras. Both models are nearly identical and applies to the same problem. But if you compare the codes you g

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import numpy as np
from scipy.cluster.vq import vq, kmeans2
from scipy.spatial.distance import cdist


def train(vec, M, Ks=256):
    Ds = int(vec.shape[1] / M)
    codeword = np.empty((M, Ks, Ds), np.float32)

    for m in range(M):

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