CNN_MNIST.rar_CNN mnist_MNIST CNN_MNIST 数据集_tensorflow cnn_卷积 m

from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np import tensorflow as tf from tensorflow.contrib import learn from tensorflow.contrib.learn.python.learn.estimators import model_fn as model_fn_lib tf.logging.set_verbosity(tf.logging.INFO) def cnn_model_fn(features, labels, mode): # Input layer input_layer = tf.reshape(features, [-1, 28, 28, 1]) # Convolutional layer 1 conv1 = tf.layers.conv2d( inputs = input_layer, filters = 32, kernel_size = [5,5], padding = "same", activation = tf.nn.relu ) # Pooling layer 1 pool1 = tf.layers.max_pooling2d( inputs = conv1, pool_size = [2,2], strides = 2 ) # Convolutional layer 2 conv2 = tf.layers.conv2d( inputs = pool1, filters = 64, kernel_size = [5,5], padding = "same", activation = tf.nn.relu ) # Pooling layer 2 pool2 = tf.layers.max_pooling2d( inputs = conv2, pool_size = [2,2], strides = 2 ) # Fully connection layer pool2_flat = tf.reshape(pool2, [-1, 7*7*64]) dense = tf.layers.dense( inputs = pool2_flat, units = 1024, activation = tf.nn.relu ) dropout = tf.layers.dropout( inputs = dense, rate = 0.1, training = mode == learn.ModeKeys.TRAIN ) # Output layer logits = tf.layers.dense( inputs = dropout,#dense units = 10 ) loss = None train_op = None # Calculate loss if mode != learn.ModeKeys.INFER: onehot_labels = tf.one_hot( indices = tf.cast(labels, tf.int32), depth = 10 ) loss = tf.losses.softmax_cross_entropy( onehot_labels = onehot_labels, logits = logits ) # Configure the training op if mode == learn.ModeKeys.TRAIN: train_op = tf.contrib.layers.optimize_loss( loss = loss, global_step = tf.contrib.framework.get_global_step(), learning_rate = 0.01, optimizer = "SGD" ) # Generate predictions predictions = { "classes":tf.argmax(input = logits, axis = 1), "probabilities":tf.nn.softmax(logits, name = "softmax_tensor") } # Return a ModelFnOps object return model_fn_lib.ModelFnOps( mode = mode, predictions = predictions, loss = loss, train_op = train_op ) def main(unused_argv): # load training and test data mnist = learn.datasets.load_dataset("mnist") train_data = mnist.train.images train_labels = np.asarray(mnist.train.labels, dtype = np.int32) test_data = mnist.test.images test_labels = np.asarray(mnist.test.labels, dtype = np.int32) # Create the estimator mnist_classifier = learn.Estimator( model_fn = cnn_model_fn, model_dir = "/tmp/mnist_convnet_model" ) # Set up logging for predictions tensors_to_log = {"probabilities": "softmax_tensor"} logging_hook = tf.train.LoggingTensorHook( tensors = tensors_to_log, every_n_iter = 50 ) # Train the model mnist_classifier.fit( x = train_data, y = train_labels, batch_size = 100, steps = 2000, monitors = [logging_hook] ) # Configure the accuracy metric for evaluation metrics = { "accuracy": learn.MetricSpec( metric_fn = tf.metrics.accuracy, prediction_key = "classes" ), } # Test the model and print results sum_results = 0 ave_results = 0 for i in range(2): test_results = mnist_classifier.evaluate( x = test_data[i*5000+1:(i+1)*5000], y = test_labels[i*5000+1:(i+1)*5000], metrics = metrics ) print(test_results) sum_results += test_results['accuracy'] ave_results = sum_results/2 print(ave_results) if __name__ == "__main__": tf.app.run()