cnn.zip_CNN 手写数据集_cnn 手写_tensorflow cnn_卷积 mnist_神经网络

from __future__ import division, print_function, absolute_import # Import MNIST data from tensorflow.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets("/tmp/data/", one_hot=False) import tensorflow as tf #import tensor_MFA # Training Parameters learning_rate = 0.001 num_steps = 1000 batch_size = 100 # Network Parameters num_input = 784 # MNIST data input (img shape: 28*28) num_classes = 10 # MNIST total classes (0-9 digits) dropout = 0.75 # Dropout, probability to keep units # Create the neural network def conv_net(x_dict, n_classes, dropout, reuse, is_training): # Define a scope for reusing the variables with tf.variable_scope('ConvNet', reuse=reuse): # TF Estimator input is a dict, in case of multiple inputs x = x_dict['images'] # MNIST data input is a 1-D vector of 784 features (28*28 pixels) # Reshape to match picture format [Height x Width x Channel] # Tensor input become 4-D: [Batch Size, Height, Width, Channel] x = tf.reshape(x, shape=[-1, 28, 28, 1]) # Convolution Layer with 32 filters and a kernel size of 5 conv1 = tf.layers.conv2d(x, 32, 5, activation=tf.nn.relu) # Max Pooling (down-sampling) with strides of 2 and kernel size of 2 conv1 = tf.layers.max_pooling2d(conv1, 2, 2) # Convolution Layer with 64 filters and a kernel size of 3 conv2 = tf.layers.conv2d(conv1, 64, 3, activation=tf.nn.relu) # Max Pooling (down-sampling) with strides of 2 and kernel size of 2 conv2 = tf.layers.max_pooling2d(conv2, 2, 2) # Flatten the data to a 1-D vector for the fully connected layer fc1 = tf.contrib.layers.flatten(conv2) # Fully connected layer (in tf contrib folder for now) fc1 = tf.layers.dense(fc1, 1024) # Apply Dropout (if is_training is False, dropout is not applied) fc1 = tf.layers.dropout(fc1, rate=dropout, training=is_training) # Output layer, class prediction out = tf.layers.dense(fc1, n_classes) return out # Define the model function (following TF Estimator Template) def model_fn(features, labels, mode): # Build the neural network # Because Dropout have different behavior at training and prediction time, we # need to create 2 distinct computation graphs that still share the same weights. logits_train = conv_net(features, num_classes, dropout, reuse=False, is_training=True) logits_test = conv_net(features, num_classes, dropout, reuse=True, is_training=False) # Predictions pred_classes = tf.argmax(logits_test, axis=1) pred_probas = tf.nn.softmax(logits_test) # If prediction mode, early return if mode == tf.estimator.ModeKeys.PREDICT: return tf.estimator.EstimatorSpec(mode, predictions=pred_classes) # Define my loss and optimizer ''' L_pose_b,L_pose_w,L_pose_smile_b,L_pose_smile_w=tensor_MFA.MFA(logits_train.T,labels,labels,10,10,5,5) F=tf.matmul((logits_train.T*L_pose_w*logits_train).I,(logits_train.T*L_pose_b*logits_train)) eigv=tf.self_adjoint_eig(F) eigvals=eigv[0,:] num=tf.floor(tf.shape(eigvals)[1]*0.5) #num=2 temp=tf.nn.top_k(eigvals, num) loss_op=tf.reduce_sum(temp.values) ''' loss_op = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits( logits=logits_train, labels=tf.cast(labels, dtype=tf.int32))) optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate) train_op = optimizer.minimize(loss_op, global_step=tf.train.get_global_step()) # Evaluate the accuracy of the model acc_op = tf.metrics.accuracy(labels=labels, predictions=pred_classes) # TF Estimators requires to return a EstimatorSpec, that specify # the different ops for training, evaluating, ... estim_specs = tf.estimator.EstimatorSpec( mode=mode, predictions=pred_classes, loss=loss_op, train_op=train_op, eval_metric_ops={'accuracy': acc_op}) return estim_specs # Build the Estimator model = tf.estimator.Estimator(model_fn) # Define the input function for training input_fn = tf.estimator.inputs.numpy_input_fn( x={'images': mnist.train.images}, y=mnist.train.labels, batch_size=batch_size, num_epochs=None, shuffle=True) # Train the Model model.train(input_fn, steps=num_steps) # Evaluate the Model # Define the input function for evaluating input_fn = tf.estimator.inputs.numpy_input_fn( x={'images': mnist.test.images}, y=mnist.test.labels, batch_size=batch_size, shuffle=False) # Use the Estimator 'evaluate' method e = model.evaluate(input_fn) print("Testing Accuracy:", e['accuracy'])