用python生成与调用cntk模型代码演示方法-创新互联
由于一些原因,视频录制要告一段落了。再写一篇关于cntk的文章分享出来吧。我也很想将这个事情进行下去。以后如果条件允许还会接着做。
成都创新互联服务热线:13518219792,为您提供成都网站建设网页设计及定制高端网站建设服务,成都创新互联网页制作领域十余年,包括成都OPP胶袋等多个行业拥有多年建站经验,选择成都创新互联,为企业保驾护航!cntk2.0框架生成的模型才可以支持python。1.0不支持。
python可以导入cntk.exe生成的框架,也可以导入python调用cntk生成的框架。举两个例子:
1 、导入cntk.exe生成的框架。
from cntk.ops.functions import load_model from PIL import Image import numpy as np from sklearn.utils import shuffle np.random.seed(0) def generate(N, mean, cov, diff): #import ipdb;ipdb.set_trace() samples_per_class = int(N/2) X0 = np.random.multivariate_normal(mean, cov, samples_per_class) Y0 = np.zeros(samples_per_class) for ci, d in enumerate(diff): X1 = np.random.multivariate_normal(mean+d, cov, samples_per_class) Y1 = (ci+1)*np.ones(samples_per_class) X0 = np.concatenate((X0,X1)) Y0 = np.concatenate((Y0,Y1)) X, Y = shuffle(X0, Y0) return X,Y mean = np.random.randn(2) cov = np.eye(2) features, labels = generate(6, mean, cov, [[3.0], [3.0, 0.0]]) features= features.astype(np.float32) labels= labels.astype(np.int) print(features) print(labels) z = load_model("MC.dnn") print(z.parameters[0].value) print(z.parameters[0]) print(z) print(z.uid) #print(z.signature) #print(z.layers[0].E.shape) #print(z.layers[2].b.value) for index in range(len(z.inputs)): print("Index {} for input: {}.".format(index, z.inputs[index])) for index in range(len(z.outputs)): print("Index {} for output: {}.".format(index, z.outputs[index].name)) import cntk as ct z_out = ct.combine([z.outputs[2].owner]) predictions = np.squeeze(z_out.eval({z_out.arguments[0]:[features]})) ret = list() for t in predictions: ret.append(np.argmax(t)) top_class = np.argmax(predictions) print(ret) print("predictions{}.top_class{}".format(predictions,top_class))
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