import csv csvfile = file('E:\\workspace\\data\\ex1.csv', 'rb') reader = csv.reader(csvfile) for line in reader: print line csvfile.close() import matplotlib import matplotlib.pyplot as plt import matplotlib.cm as cm from urllib import urlretrieve import cPickle as pickle import os import gzip import numpy as np import theano import lasagne from lasagne import layers from lasagne.updates import nesterov_momentum from nolearn.lasagne import NeuralNet from nolearn.lasagne import visualize from sklearn.metrics import classification_report from sklearn.metrics import confusion_matrix def load_dataset(): url = 'http://deeplearning.net/data/mnist/mnist.pkl.gz' filename = 'E:\\data\\mnist.pkl.gz' if not os.path.exists(filename): print("Downloading MNIST dataset...") urlretrieve(url, filename) with gzip.open(filename, 'rb') as f: data = pickle.load(f) X_train, y_train = data[0] X_val, y_val = data[1] X_test, y_test = data[2] X_train = X_train.reshape((-1, 1, 28, 28)) X_val = X_val.reshape((-1, 1, 28, 28)) X_test = X_test.reshape((-1, 1, 28, 28)) y_train = y_train.astype(np.uint8) y_val = y_val.astype(np.uint8) y_test = y_test.astype(np.uint8) return X_train, y_train, X_val, y_val, X_test, y_test X_train, y_train, X_val, y_val, X_test, y_test = load_dataset() plt.imshow(X_train[0][0], cmap=cm.binary) net1 = NeuralNet( layers=[('input', layers.InputLayer), ('conv2d1', layers.Conv2DLayer), ('maxpool1', layers.MaxPool2DLayer), ('conv2d2', layers.Conv2DLayer), ('maxpool2', layers.MaxPool2DLayer), ('dropout1', layers.DropoutLayer), ('dense', layers.DenseLayer), ('dropout2', layers.DropoutLayer), ('output', layers.DenseLayer), ], # input layer input_shape=(None, 1, 28, 28), # layer conv2d1 conv2d1_num_filters=32, conv2d1_filter_size=(5, 5), conv2d1_nonlinearity=lasagne.nonlinearities.rectify, conv2d1_W=lasagne.init.GlorotUniform(), # layer maxpool1 maxpool1_pool_size=(2, 2), # layer conv2d2 conv2d2_num_filters=32, conv2d2_filter_size=(5, 5), conv2d2_nonlinearity=lasagne.nonlinearities.rectify, # layer maxpool2 maxpool2_pool_size=(2, 2), # dropout1 dropout1_p=0.5, # dense dense_num_units=256, dense_nonlinearity=lasagne.nonlinearities.rectify, # dropout2 dropout2_p=0.5, # output output_nonlinearity=lasagne.nonlinearities.softmax, output_num_units=10, # optimization method params update=nesterov_momentum, update_learning_rate=0.01, update_momentum=0.9, max_epochs=10, verbose=1, ) # Train the network nn = net1.fit(X_train, y_train) preds = net1.predict(X_test) cm = confusion_matrix(y_test, preds) plt.matshow(cm) plt.title('Confusion matrix') plt.colorbar() plt.ylabel('True label') plt.xlabel('Predicted label') plt.show()
Python 操作.csv文件
原创sherry20150511 博主文章分类:python ©著作权
文章标签 Python .csv 文章分类 Python 后端开发
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