convolutional neural network tensorflow

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Learning notes TF057: TensorFlow MNIST, convolutional neural network, recurrent neural network, unsupervised learning, tf057tensorflow

Learning notes TF057: TensorFlow MNIST, convolutional neural network, recurrent neural network, unsupervised learning, tf057tensorflow MNIST convolutional

"TensorFlow Combat" tensorflow realization of the classical convolutional neural network vggnet

(): Image_size= 224Images=TF. Variable (Tf.random_normal ([Batch_size, Image_size, Image_size,3], Dtype=Tf.float32, StdDev=1e-1)) Keep_prob=Tf.placeholder (tf.float32) predictions, Softmax, FC8, p=inference_op (images, keep_prob) init=tf.global_variables_initializer () config=TF. Configproto () Config.gpu_options.allocator_type='BFC'Sess= TF. Session (config=config) sess.run (init) time_tensorflow_run (sess, predictions, {keep_prob:1.0},"Forward") O

Tensorflow32 "TensorFlow Combat" note -05 TensorFlow realize convolutional neural Network code

01 Simple Convolution network # "TensorFlow Combat" TensorFlow realize convolution neural network # WIN10 Tensorflow1.0.1 python3.5.3 # CUDA v8.0 cudnn-8.0-windows10-x64-v5.1 # Filen ame:sz05.01.py # Simple convolution network

Learning Note TF052: convolutional networks, neural network development, alexnet TensorFlow implementation

= Mnist.train.next_batch (batch_size)Sess.run (Optimizer, feed_dict={x:batch_x, y:batch_y, keep_prob:dropout})If step% Display_step = = 0:# Calculate loss value and accuracy, outputLoss, acc = Sess.run ([cost, accuracy], feed_dict={x:batch_x, Y:batch_y, Keep_prob:1.})Print "Iter" + str (step*batch_size) + ", Minibatch loss=" + "{:. 6f}". Format (Loss) + ", Training accuracy=" + "{:. 5f}". f Ormat (ACC)Step + = 1Print "Optimization finished!"# Calculate Test AccuracyPrint "Testing accuracy:", se

TensorFlow deep learning convolutional neural network CNN, tensorflowcnn

TensorFlow deep learning convolutional neural network CNN, tensorflowcnn I. Convolutional Neural Network Overview ConvolutionalNeural Network

TensorFlow Training Mnist DataSet (3)--convolutional neural network

disconnects the connection arcs between certain nodes, so that they do not participate in the training for the time being.2. Data preprocessingThe data used for training is read first. from Import = input_data.read_data_sets ('./data/mnist', one_hot=true)In the preceding input layer, each sample entered is one-dimensional data, and the sample data of the convolutional neural

TensorFlow Study Note Five: mnist example-convolutional neural Network (CNN)

The mnist examples of convolutional neural networks and the neural network examples in the previous blog post are mostly the same. But CNN has more layers, and the network model needs to be built on its own.The procedure is more complicated, I will be divided into several pa

Tensorflow-based CNN convolutional neural network classifier for fasion-mnist Dataset

: test_features, y: test_labes}))sess.close() 1. Define weight, biases, Conv layer, pool Layer def Weight(shape): initial = tf.truncated_normal(shape, stddev=0.1) return tf.Variable(initial, tf.float32)def biases(shape): initial = tf.constant(0.1, shape=shape) return tf.Variable(initial, tf.float32)def conv(inputs, w): return tf.nn.conv2d(inputs, w, strides=[1, 1, 1, 1], padding=‘SAME‘)def pool(inputs): return tf.nn.max_pool(inputs, ksize=[1, 1, 1, 1], strides=[1, 2, 2, 1], pa

Deeplearning.ai the first week of class fourth, the TensorFlow realization of convolutional neural network

, n_y): "" " creates the Placeholders for the TensorFlow session. Arguments: n_h0-scalar, height of an input image n_w0-scalar, width of an input image n_c0-scalar, nu Mber of channels of the input n_y-scalar, number of classes Returns: X--placeholder for the data input, O f shape [None, N_h0, N_w0, n_c0] and Dtype "float" Y--placeholder for the input labels, of shape [None, n_y] and DT Ype "float" "" " # # #

convolutional Neural Network (convolutional neural network,cnn)

between the filter parameters are not the same.) Sharing the parameters of the filter allows the content in the image to be unaffected by the position. Take mnist handwritten numeral recognition as an example, whether the number "1" appears in the upper left or bottom right corner, the type of picture is unchanged. Sharing the parameters of the convolution filter can also drastically reduce the parameters on the neural

convolutional Neural Network (convolutional neural network,cnn)

The biggest problem with full-attached neural networks (Fully connected neural network) is that there are too many parameters for the full-connection layer. In addition to slowing down the calculation, it is easy to cause overfitting problems. Therefore, a more reasonable neural ne

"Original" Van Gogh oil painting with deep convolutional neural network What is the effect of 100,000 iterations? A neural style of convolutional neural networks

As a free from the vulgar Code of the farm, the Spring Festival holiday Idle, decided to do some interesting things to kill time, happened to see this paper: A neural style of convolutional neural networks, translated convolutional neural

convolutional Neural Networks convolutional neural Network (II.)

Transfer from http://blog.csdn.net/zouxy09/article/details/8781543CNNs is the first learning algorithm to truly successfully train a multi-layered network structure. It uses spatial relationships to reduce the number of parameters that need to be learned to improve the training performance of the general Feedforward BP algorithm. In CNN, a small part of the image (local sensing area) as the lowest layer of the input of the hierarchy, the information i

(reproduced) convolutional Neural Networks convolutional neural network

convolutional Neural Networks convolutional neural network contents One: Leading back propagation reverse propagation algorithm Network structure Learning Algorithms Two:

UFLDL Learning notes and programming Jobs: convolutional neural Network (convolutional neural Networks)

UFLDL Learning notes and programming Jobs: convolutional neural Network (convolutional neural Networks)UFLDL out a new tutorial, feel better than before, from the basics, the system is clear, but also programming practice.In deep learning high-quality group inside listen to

convolutional neural Network (ii): convolutional neural network BP algorithm for CNN

,In the above formula, the * number is the convolution operation, the kernel function k is rotated 180 degrees and then the error term is related to the operation, and then summed.Finally, we study how to calculate the partial derivative of the kernel function connected with the convolution layer after obtaining the error terms of each layer, and the formula is as follows.The partial derivative of the kernel function can be obtained when the error item of the convolution layer is rotated 180 deg

convolutional Neural Networks (convolutional neural Network)

convolutional neural network for CNN. The C-layer represents all the layers that are obtained after filtering the input image, also called "convolution layer". The S layer represents the layer that the input image is sampled (subsampling) to get. Where C1 and C3 are convolution layers, S2 and S4 are the next sampling layers. Each layer in the C, S layer consists

Deep learning Note (i) convolutional neural network (convolutional neural Networks)

I. Convolutionconvolutional Neural Networks (convolutional neural Networks) are neural networks that share parameters spatially. Multiply by using a number of layers of convolution, rather than a matrix of layers. In the process of image processing, each picture can be regarded as a "pancake", which includes the height

convolutional Neural Network (convolutional neural Networks)

convolutional neural Network (CNN) is the foundation of deep learning. The traditional fully-connected neural network (fully connected networks) takes numerical values as input.If you want to work with image-related information, you should also extract the features from the

The parallelization model of convolutional neural network--one weird trick for parallelizing convolutional neural Networks

I've been focusing on CNN implementations for a while, looking at Caffe's code and Convnet2 's code. At present, the content of the single-machine multi-card is more interested, so pay special attention to Convnet2 about MULTI-GPU support.where Cuda-convnet2 's project address is published in: Google Code:cuda-convnet2A more important paper on MULTI-GPU is: one weird trick for parallelizing convolutional neural

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