具体的网址在这里：
https://github.com/tensorflow/tensorflow/tree/r0.12/tensorflow/models
一个卷积神经网络用于股票分析的例子: https://github.com/keon/deepstock, https://github.com/keon/deepstock
import argparse import gzip import os import sys import time import numpy import tensorflow as tf SOURCE_URL = 'http://yann.lecun.com/exdb/mnist/' WORK_DIRECTORY = '/home/hzh/tf' IMAGE_SIZE = 28 NUM_CHANNELS = 1 PIXEL_DEPTH = 255 NUM_LABELS = 10 VALIDATION_SIZE = 5000 # Size of the validation set. SEED = 66478 # Set to None for random seed. BATCH_SIZE = 64 NUM_EPOCHS = 10 EVAL_BATCH_SIZE = 64 EVAL_FREQUENCY = 100 # Number of steps between evaluations. FLAGS = None def data_type(): """Return the type of the activations, weights, and placeholder variables.""" if FLAGS.use_fp16: return tf.float16 else: return tf.float32 def maybe_download(filename): """Download the data from Yann's website, unless it's already here.""" if not tf.gfile.Exists(WORK_DIRECTORY): tf.gfile.MakeDirs(WORK_DIRECTORY) filepath = os.path.join(WORK_DIRECTORY, filename) return filepath def extract_data(filename, num_images): """Extract the images into a 4D tensor [image index, y, x, channels]. Values are rescaled from [0, 255] down to [-0.5, 0.5]. """ print('Extracting', filename) with gzip.open(filename) as bytestream: bytestream.read(16) buf = bytestream.read(IMAGE_SIZE * IMAGE_SIZE * num_images * NUM_CHANNELS) data = numpy.frombuffer(buf, dtype=numpy.uint8).astype(numpy.float32) data = (data - (PIXEL_DEPTH / 2.0)) / PIXEL_DEPTH data = data.reshape(num_images, IMAGE_SIZE, IMAGE_SIZE, NUM_CHANNELS) return data def extract_labels(filename, num_images): """Extract the labels into a vector of int64 label IDs.""" print('Extracting', filename) with gzip.open(filename) as bytestream: bytestream.read(8) buf = bytestream.read(1 * num_images) labels = numpy.frombuffer(buf, dtype=numpy.uint8).astype(numpy.int64) return labels def fake_data(num_images): """Generate a fake dataset that matches the dimensions of MNIST.""" data = numpy.ndarray( shape=(num_images, IMAGE_SIZE, IMAGE_SIZE, NUM_CHANNELS), dtype=numpy.float32) labels = numpy.zeros(shape=(num_images,), dtype=numpy.int64) for image in range(num_images): label = image % 2 data[image, :, :, 0] = label - 0.5 labels[image] = label return data, labels def error_rate(predictions, labels): """Return the error rate based on dense predictions and sparse labels.""" return 100.0 - ( 100.0 * numpy.sum(numpy.argmax(predictions, 1) == labels) / predictions.shape[0]) def main(_): if FLAGS.self_test: print('Running self-test.') train_data, train_labels = fake_data(256) validation_data, validation_labels = fake_data(EVAL_BATCH_SIZE) test_data, test_labels = fake_data(EVAL_BATCH_SIZE) num_epochs = 1 else: # Get the data. train_data_filename = maybe_download('train-images-idx3-ubyte.gz') train_labels_filename = maybe_download('train-labels-idx1-ubyte.gz') test_data_filename = maybe_download('t10k-images-idx3-ubyte.gz') test_labels_filename = maybe_download('t10k-labels-idx1-ubyte.gz') # Extract it into numpy arrays. train_data = extract_data(train_data_filename, 60000) train_labels = extract_labels(train_labels_filename, 60000) test_data = extract_data(test_data_filename, 10000) test_labels = extract_labels(test_labels_filename, 10000) # Generate a validation set. validation_data = train_data[:VALIDATION_SIZE, ...] validation_labels = train_labels[:VALIDATION_SIZE] train_data = train_data[VALIDATION_SIZE:, ...] train_labels = train_labels[VALIDATION_SIZE:] num_epochs = NUM_EPOCHS train_size = train_labels.shape[0] # This is where training samples and labels are fed to the graph. # These placeholder nodes will be fed a batch of training data at each # training step using the {feed_dict} argument to the Run() call below. train_data_node = tf.placeholder( data_type(), shape=(BATCH_SIZE, IMAGE_SIZE, IMAGE_SIZE, NUM_CHANNELS)) train_labels_node = tf.placeholder(tf.int64, shape=(BATCH_SIZE,)) eval_data = tf.placeholder( data_type(), shape=(EVAL_BATCH_SIZE, IMAGE_SIZE, IMAGE_SIZE, NUM_CHANNELS)) # The variables below hold all the trainable weights. They are passed an # initial value which will be assigned when we call: # {tf.global_variables_initializer().run()} conv1_weights = tf.Variable( tf.truncated_normal([5, 5, NUM_CHANNELS, 32], # 5x5 filter, depth 32. stddev=0.1, seed=SEED, dtype=data_type())) conv1_biases = tf.Variable(tf.zeros([32], dtype=data_type())) conv2_weights = tf.Variable(tf.truncated_normal( [5, 5, 32, 64], stddev=0.1, seed=SEED, dtype=data_type())) conv2_biases = tf.Variable(tf.constant(0.1, shape=[64], dtype=data_type())) fc1_weights = tf.Variable( # fully connected, depth 512. tf.truncated_normal([IMAGE_SIZE // 4 * IMAGE_SIZE // 4 * 64, 512], stddev=0.1, seed=SEED, dtype=data_type())) fc1_biases = tf.Variable(tf.constant(0.1, shape=[512], dtype=data_type())) fc2_weights = tf.Variable(tf.truncated_normal([512, NUM_LABELS], stddev=0.1, seed=SEED, dtype=data_type())) fc2_biases = tf.Variable(tf.constant(0.1, shape=[NUM_LABELS], dtype=data_type())) # We will replicate the model structure for the training subgraph, as well # as the evaluation subgraphs, while sharing the trainable parameters. def model(data, train=False): """The Model definition.""" # 2D convolution, with 'SAME' padding (i.e. the output feature map has # the same size as the input). Note that {strides} is a 4D array whose # shape matches the data layout: [image index, y, x, depth]. conv = tf.nn.conv2d(data, conv1_weights, strides=[1, 1, 1, 1], padding='SAME') # Bias and rectified linear non-linearity. relu = tf.nn.relu(tf.nn.bias_add(conv, conv1_biases)) # Max pooling. The kernel size spec {ksize} also follows the layout of # the data. Here we have a pooling window of 2, and a stride of 2. pool = tf.nn.max_pool(relu, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME') conv = tf.nn.conv2d(pool, conv2_weights, strides=[1, 1, 1, 1], padding='SAME') relu = tf.nn.relu(tf.nn.bias_add(conv, conv2_biases)) pool = tf.nn.max_pool(relu, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME') # Reshape the feature map cuboid into a 2D matrix to feed it to the # fully connected layers. pool_shape = pool.get_shape().as_list() reshape = tf.reshape( pool, [pool_shape[0], pool_shape[1] * pool_shape[2] * pool_shape[3]]) # Fully connected layer. Note that the '+' operation automatically # broadcasts the biases. hidden = tf.nn.relu(tf.matmul(reshape, fc1_weights) + fc1_biases) # Add a 50% dropout during training only. Dropout also scales # activations such that no rescaling is needed at evaluation time. if train: hidden = tf.nn.dropout(hidden, 0.5, seed=SEED) return tf.matmul(hidden, fc2_weights) + fc2_biases # Training computation: logits + cross-entropy loss. logits = model(train_data_node, True) loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=train_labels_node)) # L2 regularization for the fully connected parameters. regularizers = (tf.nn.l2_loss(fc1_weights) + tf.nn.l2_loss(fc1_biases) + tf.nn.l2_loss(fc2_weights) + tf.nn.l2_loss(fc2_biases)) # Add the regularization term to the loss. loss += 5e-4 * regularizers # Optimizer: set up a variable that's incremented once per batch and # controls the learning rate decay. batch = tf.Variable(0, dtype=data_type()) # Decay once per epoch, using an exponential schedule starting at 0.01. learning_rate = tf.train.exponential_decay( 0.01, # Base learning rate. batch * BATCH_SIZE, # Current index into the dataset. train_size, # Decay step. 0.95, # Decay rate. staircase=True) # Use simple momentum for the optimization. optimizer = tf.train.MomentumOptimizer(learning_rate, 0.9).minimize(loss, global_step=batch) # Predictions for the current training minibatch. train_prediction = tf.nn.softmax(logits) # Predictions for the test and validation, which we'll compute less often. eval_prediction = tf.nn.softmax(model(eval_data)) # Small utility function to evaluate a dataset by feeding batches of data to # {eval_data} and pulling the results from {eval_predictions}. # Saves memory and enables this to run on smaller GPUs. def eval_in_batches(data, sess): """Get all predictions for a dataset by running it in small batches.""" size = data.shape[0] if size < EVAL_BATCH_SIZE: raise ValueError("batch size for evals larger than dataset: %d" % size) predictions = numpy.ndarray(shape=(size, NUM_LABELS), dtype=numpy.float32) for begin in range(0, size, EVAL_BATCH_SIZE): end = begin + EVAL_BATCH_SIZE if end <= size: predictions[begin:end, :] = sess.run( eval_prediction, feed_dict={eval_data: data[begin:end, ...]}) else: batch_predictions = sess.run( eval_prediction, feed_dict={eval_data: data[-EVAL_BATCH_SIZE:, ...]}) predictions[begin:, :] = batch_predictions[begin - size:, :] return predictions # Create a local session to run the training. start_time = time.time() with tf.Session() as sess: # Run all the initializers to prepare the trainable parameters. tf.global_variables_initializer().run() print('Initialized!') # Loop through training steps. for step in range(int(num_epochs * train_size) // BATCH_SIZE): # Compute the offset of the current minibatch in the data. # Note that we could use better randomization across epochs. offset = (step * BATCH_SIZE) % (train_size - BATCH_SIZE) batch_data = train_data[offset:(offset + BATCH_SIZE), ...] batch_labels = train_labels[offset:(offset + BATCH_SIZE)] # This dictionary maps the batch data (as a numpy array) to the # node in the graph it should be fed to. feed_dict = {train_data_node: batch_data, train_labels_node: batch_labels} # Run the optimizer to update weights. sess.run(optimizer, feed_dict=feed_dict) # print some extra information once reach the evaluation frequency if step % EVAL_FREQUENCY == 0: # fetch some extra nodes' data l, lr, predictions = sess.run([loss, learning_rate, train_prediction], feed_dict=feed_dict) elapsed_time = time.time() - start_time start_time = time.time() print('Step %d (epoch %.2f), %.1f ms' % (step, float(step) * BATCH_SIZE / train_size, 1000 * elapsed_time / EVAL_FREQUENCY)) print('Minibatch loss: %.3f, learning rate: %.6f' % (l, lr)) print('Minibatch error: %.1f%%' % error_rate(predictions, batch_labels)) print('Validation error: %.1f%%' % error_rate(eval_in_batches(validation_data, sess), validation_labels)) sys.stdout.flush() # Finally print the result! test_error = error_rate(eval_in_batches(test_data, sess), test_labels) print('Test error: %.1f%%' % test_error) if FLAGS.self_test: print('test_error', test_error) assert test_error == 0.0, 'expected 0.0 test_error, got %.2f' % (test_error,) if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument( '--use_fp16', default=False, help='Use half floats instead of full floats if True.', action='store_true') parser.add_argument( '--self_test', default=False, action='store_true', help='True if running a self test.') FLAGS, unparsed = parser.parse_known_args() tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)
网址里面还有很多其它的示例，这些示例代码是最全的，比google网站上的还全，也比 github 上最新的 tensorflow 的库的例子要全要好 。