Python tensorflow.truncated_normal函数代码示例

 def testNoCSE(self):
   with self.test_session(use_gpu=True):
     shape = [2, 3, 4]
     rnd1 = tf.truncated_normal(shape, 0.0, 1.0, tf.float32)
     rnd2 = tf.truncated_normal(shape, 0.0, 1.0, tf.float32)
     diff = rnd2 - rnd1
     self.assertTrue(np.linalg.norm(diff.eval()) > 0.1)

    def fc_layers(self):
        # fc1
        with tf.name_scope('fc1') as scope:
            shape = int(np.prod(self.pool5.get_shape()[1:]))
            fc1w = tf.Variable(tf.truncated_normal([shape, 4096],
                                                         dtype=tf.float32,
                                                         stddev=1e-1), name='weights')
            fc1b = tf.Variable(tf.constant(1.0, shape=[4096], dtype=tf.float32),
                                 trainable=True, name='biases')
            pool5_flat = tf.reshape(self.pool5, [-1, shape])
            fc1l = tf.nn.bias_add(tf.matmul(pool5_flat, fc1w), fc1b)
            self.fc1 = tf.nn.relu(fc1l)
            self.parameters += [fc1w, fc1b]

        # fc2
        with tf.name_scope('fc2') as scope:
            fc2w = tf.Variable(tf.truncated_normal([4096, 4096],
                                                         dtype=tf.float32,
                                                         stddev=1e-1), name='weights')
            fc2b = tf.Variable(tf.constant(1.0, shape=[4096], dtype=tf.float32),
                                 trainable=True, name='biases')
            fc2l = tf.nn.bias_add(tf.matmul(self.fc1, fc2w), fc2b)
            self.fc2 = tf.nn.relu(fc2l)
            self.parameters += [fc2w, fc2b]

        # fc3
        with tf.name_scope('fc3') as scope:
            fc3w = tf.Variable(tf.truncated_normal([4096, 1000],
                                                         dtype=tf.float32,
                                                         stddev=1e-1), name='weights')
            fc3b = tf.Variable(tf.constant(1.0, shape=[1000], dtype=tf.float32),
                                 trainable=True, name='biases')
            self.fc3l = tf.nn.bias_add(tf.matmul(self.fc2, fc3w), fc3b)
            self.parameters += [fc3w, fc3b]

    def __init__(self, input_size, num_hidden, minibatch_size, name='lstmcell'):
        """
        Constructs an LSTM Cell

        Parameters:
        ----------
        input_size: int
            the size of the single input vector to the cell
        num_hidden: int
            the number of hidden nodes in the cell
        minibatch_size: int
            the number of the input vectors in the input matrix
        """

        LSTMCell.created_count += 1

        self.id = name + '_' + str(LSTMCell.created_count) if name == 'lstmcell' else name

        self.input_size = input_size
        self.num_hidden = num_hidden
        self.minibatch_size = minibatch_size

        self.input_weights = tf.Variable(tf.truncated_normal([self.input_size, self.num_hidden * 4], -0.1, 0.1), name=self.id + '_wi')
        self.output_weights = tf.Variable(tf.truncated_normal([self.num_hidden, self.num_hidden * 4], -0.1, 0.1), name=self.id + '_wo')
        self.bias = tf.Variable(tf.zeros([self.num_hidden * 4]), name=self.id + '_b')

        self.prev_output = tf.Variable(tf.zeros([self.minibatch_size, self.num_hidden]), trainable=False, name=self.id+'_o')
        self.prev_state = tf.Variable(tf.zeros([self.minibatch_size, self.num_hidden]), trainable=False, name=self.id+'_s')

def autoencoder(d,c=5,tied_weights=False):
	'''
	An autoencoder network with one hidden layer (containing the encoding),
	and sigmoid activation functions.

	Args:
		d: dimension of input.
		c: dimension of code.
		tied_weights: True if w1^T=w2
	Returns:
		Dictionary containing input placeholder Tensor and loss Variable
	Raises:
	'''
	inputs = tf.placeholder(tf.float32, shape=[None,d], name='input')

	w1 = tf.Variable(tf.truncated_normal([d,c], stddev=1.0/math.sqrt(d)))
	b1 = tf.Variable(tf.zeros([c]))

	w2 = tf.Variable(tf.truncated_normal([c,d], stddev=1.0/math.sqrt(c)))
	# TODO: Implement tied weights
	b2 = tf.Variable(tf.zeros([d]))

	code = tf.nn.sigmoid(tf.matmul(inputs, w1)+b1, name='encoding')
	reconstruction = tf.nn.sigmoid(tf.matmul(code, w2)+b2, name='reconstruction')
	loss = tf.reduce_mean(tf.square(reconstruction - inputs))
	tf.scalar_summary('loss', loss)
	return {'inputs': inputs, 'loss': loss}

def runNN (train_x, train_y, test_x, test_y, numHidden):
	print "NN({})".format(numHidden)
	session = tf.InteractiveSession()

	x = tf.placeholder("float", shape=[None, train_x.shape[1]])
	y_ = tf.placeholder("float", shape=[None, 2])

	W1 = tf.Variable(tf.truncated_normal([train_x.shape[1],numHidden], stddev=0.01))
	b1 = tf.Variable(tf.truncated_normal([numHidden], stddev=0.01))
	W2 = tf.Variable(tf.truncated_normal([numHidden,2], stddev=0.01))
	b2 = tf.Variable(tf.truncated_normal([2], stddev=0.01))

	z = tf.nn.relu(tf.matmul(x,W1) + b1)
	y = tf.nn.softmax(tf.matmul(z,W2) + b2)

	cross_entropy = -tf.reduce_sum(y_*tf.log(tf.clip_by_value(y,1e-10,1.0)))
	#cross_entropy = -tf.reduce_sum(y_*tf.log(y))
	train_step = tf.train.MomentumOptimizer(learning_rate=.001, momentum=0.1).minimize(cross_entropy)
	#train_step = tf.train.AdamOptimizer(learning_rate=.01).minimize(cross_entropy)

	session.run(tf.initialize_all_variables())
	for i in range(NUM_EPOCHS):
		offset = i*BATCH_SIZE % (train_x.shape[0] - BATCH_SIZE)
		train_step.run({x: train_x[offset:offset+BATCH_SIZE, :], y_: makeLabels(train_y[offset:offset+BATCH_SIZE])})
		if i % 100 == 0:
			util.showProgress(cross_entropy, x, y, y_, test_x, test_y)
	session.close()

def create_subsample_layers():
    print('Defining parameters ...')

    for op in conv_ops:
        if 'fulcon' in op:
            #we don't create weights biases for fully connected layers because we need to calc the
            #fan_out of the last convolution/pooling (subsampling) layer
            #as that's gonna be fan_in for the 1st hidden layer
            break
        if 'conv' in op:
            print('\tDefining weights and biases for %s (weights:%s)'%(op,hyparams[op]['weights']))
            print('\t\tWeights:%s'%hyparams[op]['weights'])
            print('\t\tBias:%d'%hyparams[op]['weights'][3])
            weights[op]=tf.Variable(
                tf.truncated_normal(hyparams[op]['weights'],
                                    stddev=2./min(5,hyparams[op]['weights'][0])
                                    )
            )
            biases[op] = tf.Variable(tf.constant(np.random.random()*0.001,shape=[hyparams[op]['weights'][3]]))
        if 'incept' in op:
            print('\n\tDefining the weights and biases for the Incept Module')
            inc_hyparams = hyparams[op]
            for k,v in inc_hyparams.items():
                if 'conv' in k:
                    w_key = op+'_'+k
                    print('\t\tParameters for %s'%w_key)
                    print('\t\t\tWeights:%s'%inc_hyparams[k]['weights'])
                    print('\t\t\tBias:%d'%inc_hyparams[k]['weights'][3])
                    weights[w_key] = tf.Variable(
                        tf.truncated_normal(inc_hyparams[k]['weights'],
                                            stddev=2./min(5,inc_hyparams[k]['weights'][0])
                                            )
                    )
                    biases[w_key] = tf.Variable(tf.constant(np.random.random()*0.0,shape=[inc_hyparams[k]['weights'][3]]))

def inference(images):
    """
    Build the MNIST model
    """

    # Hidden 1
    with tf.name_scope('hidden1'):
        weights = tf.Variable(
            tf.truncated_normal([IMAGE_PIXELS, LAYER_SIZE],
                                stddev= 1.0 / math.sqrt(float(IMAGE_PIXELS))),
        name='weights')
        biases = tf.Variable(tf.zeros([LAYER_SIZE]),
                             name='biases')
        hidden1 = tf.nn.relu(tf.matmul(images, weights) + biases)
        # Add summary ops to collect data
        tf.histogram_summary('weights', weights)
        tf.histogram_summary('biases', biases)

    # Output Layer - is this correct? does this layer have any weights?
    with tf.name_scope('softmax_linear'):
        weights = tf.Variable(
            tf.truncated_normal([LAYER_SIZE, NUM_CLASSES],
                                stddev=1.0 / math.sqrt(float(LAYER_SIZE))),
            name='weights')
        biases = tf.Variable(tf.zeros([NUM_CLASSES]),
                             name='biases')
        logits = logSoftMax(tf.matmul(hidden1, weights) + biases)
        return logits

def inference(images):
  """Build the MNIST model up to where it may be used for inference.
  Args:
    images: Images placeholder, from inputs().
    hidden1_units: Size of the first hidden layer.
    hidden2_units: Size of the second hidden layer.
  Returns:
    softmax_linear: Output tensor with the computed logits.
  """
  # Hidden 1
  with tf.name_scope('conv1'):
      images=tf.reshape(images,[-1,32,32,3])
      conv1_w=tf.Variable(tf.truncated_normal([5,5,3,8]),name='weights')
      conv1_b=tf.Variable(tf.zeros([8]),name='bias')
      conv1=tf.nn.relu(tf.nn.conv2d(images,conv1_w,[1,1,1,1],'VALID')+conv1_b)
  with tf.name_scope('conv2'):
      conv2_w = tf.Variable(tf.truncated_normal([5, 5, 8, 8]), name='weights')
      conv2_b = tf.Variable(tf.zeros([8]), name='bias')
      conv2 = tf.nn.relu(tf.nn.conv2d(conv1, conv2_w, strides=[1, 1, 1, 1],padding='VALID') + conv2_b)
      conv2_flatten=tf.reshape(conv2,[-1,24*24*8])
  with tf.name_scope('softmax_linear'):
    weights = tf.Variable(
        tf.truncated_normal([24*24*8, NUM_CLASSES]),name='weights')
    biases = tf.Variable(tf.zeros([NUM_CLASSES]),name='biases')
    logits = tf.matmul(conv2_flatten, weights) + biases
  return logits

  def __init__(self, embedding_length):
    self._embedding_length = embedding_length
    self._named_tensors = {}

    for n in xrange(10):
      # Note: the examples only have the numbers 0 through 9 as terminal nodes.
      name = 'terminal_' + str(n)
      self._named_tensors[name] = tf.Variable(
          tf.truncated_normal([embedding_length],
                              dtype=tf.float32,
                              stddev=1),
          name=name)

    self._combiner_weights = {}
    self._loom_ops = {}
    for name in calculator_pb2.CalculatorExpression.OpCode.keys():
      weights_var = tf.Variable(
          tf.truncated_normal([2 * embedding_length, embedding_length],
                              dtype=tf.float32,
                              stddev=1),
          name=name)
      self._combiner_weights[name] = weights_var
      self._loom_ops[name] = CombineLoomOp(2, embedding_length, weights_var)

    self._loom = loom.Loom(
        named_tensors=self._named_tensors,
        named_ops=self._loom_ops)

    self._output = self._loom.output_tensor(
        loom.TypeShape('float32', [embedding_length]))

def train(mnist):
    x = tf.placeholder(tf.float32, [None, INPUT_NODE], name='x-input')
    y_ = tf.placeholder(tf.float32, [None, OUTPUT_NODE], name='y-input')
    weights1 = tf.Variable(tf.truncated_normal([INPUT_NODE, LAYER1_NODE], stddev=0.1))
    bias1 = tf.Variable(tf.constant(0.0, shape=[LAYER1_NODE]))
    weights2 = tf.Variable(tf.truncated_normal([LAYER1_NODE, OUTPUT_NODE], stddev=0.1))
    bias2 = tf.Variable(tf.constant(0.0, shape=[OUTPUT_NODE]))
    layer1 = tf.nn.relu(tf.matmul(x, weights1) + bias1)
    y = tf.matmul(layer1, weights2) + bias2
    global_step = tf.Variable(0, trainable=False)
    cross_entropy = tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y)
    loss = tf.reduce_mean(cross_entropy)
    train_op=tf.train.GradientDescentOptimizer(LEARNING_RATE).minimize(loss, global_step=global_step)
    correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_,1))
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
  
    with tf.Session() as sess:
        tf.initialize_all_variables().run()
        validate_feed = {x: mnist.validation.images, y_: mnist.validation.labels}
        test_feed = {x: mnist.test.images, y_: mnist.test.labels}     

        for i in range(TRAINING_STEPS):
            if i % 1000 == 0:
                validate_acc = sess.run(accuracy, feed_dict=validate_feed)
                print("After %d training step(s), validation accuracy using average model is %g " % (i, validate_acc))
            

            xs, ys = mnist.train.next_batch(BATCH_SIZE)
            sess.run(train_op, feed_dict={x: xs, y_: ys})


        test_acc = sess.run(accuracy, feed_dict=test_feed)
        print("After %d training step(s), test accuracy using average model is %g" % (TRAINING_STEPS, test_acc))

    def __init__(self, optimizer, categories, num_of_terms, num_of_hidden_nodes):
        self.optimizer           = optimizer
        self.categories          = categories
        self.num_of_categories   = len(self.categories)
        self.num_of_terms        = num_of_terms
        self.num_of_hidden_nodes = num_of_hidden_nodes

        self.input_ph      = tf.placeholder(tf.float32, [None, self.num_of_terms], name="input")
        self.supervisor_ph = tf.placeholder(tf.float32, [None, self.num_of_categories], name="supervisor")

        with tf.name_scope("inference") as scope:
            weight1_var    = tf.Variable(tf.truncated_normal([self.num_of_terms, self.num_of_hidden_nodes], stddev=0.1), name="weight1")
            weight2_var    = tf.Variable(tf.truncated_normal([self.num_of_hidden_nodes, self.num_of_categories], stddev=0.1), name="weight2")
            bias1_var      = tf.Variable(tf.zeros([self.num_of_hidden_nodes]), name="bias1")
            bias2_var      = tf.Variable(tf.zeros([self.num_of_categories]), name="bias2")
            hidden_op      = tf.nn.relu(tf.matmul(self.input_ph, weight1_var) + bias1_var)
            self.output_op = tf.nn.softmax(tf.matmul(hidden_op, weight2_var) + bias2_var)

        with tf.name_scope("loss") as scope:
            cross_entropy = -tf.reduce_sum(self.supervisor_ph * tf.log(self.output_op))
            l2_sqr        = tf.nn.l2_loss(weight1_var) + tf.nn.l2_loss(weight2_var)
            lambda_2      = 0.01
            self.loss_op  = cross_entropy + lambda_2 * l2_sqr
            tf.scalar_summary("loss", self.loss_op)

        with tf.name_scope("training") as scope:
            self.training_op = self.optimizer.minimize(self.loss_op)

        with tf.name_scope("accuracy") as scope:
            correct_prediction = tf.equal(tf.argmax(self.output_op, 1), tf.argmax(self.supervisor_ph, 1))
            self.accuracy_op   = tf.reduce_mean(tf.cast(correct_prediction, "float"))
            tf.scalar_summary("accuracy", self.accuracy_op)

        self.summary_op = tf.merge_all_summaries()

def mnist_inference(images, hidden1_units):
    """Build the MNIST model up to where it may be used for inference.
    Args:
        images: Images placeholder.
        hidden1_units: Size of the first hidden layer.
    Returns:
        logits: Output tensor with the computed logits.
    """
    # Hidden 1
    with tf.name_scope('hidden1'):
        weights = tf.Variable(
            tf.truncated_normal([IMAGE_PIXELS, hidden1_units],
                                stddev=1.0 / math.sqrt(float(IMAGE_PIXELS))),
            name='weights')
        biases = tf.Variable(tf.zeros([hidden1_units]),
                             name='biases')
        hidden1 = tf.nn.relu(tf.matmul(images, weights) + biases)
    # Linear
    with tf.name_scope('softmax_linear'):
        weights = tf.Variable(
            tf.truncated_normal([hidden1_units, NUM_CLASSES],
                                stddev=1.0 / math.sqrt(float(hidden1_units))),
            name='weights')
        biases = tf.Variable(tf.zeros([NUM_CLASSES]),
                             name='biases')
        logits = tf.matmul(hidden1, weights) + biases

    # Uncomment the following line to see what we have constructed.
    # tf.train.write_graph(tf.get_default_graph().as_graph_def(),
    #                      "/tmp", "inference.pbtxt", as_text=True)
    return logits

def inference(images, hidden1_units):
    """Build the MNIST model up to where it may be used for inference.
    
    Args:
      images: Images placeholder, from inputs().
      hidden1_units: Size of the first hidden layer.    
    Returns:
      softmax_linear: Output tensor with the computed logits.
    """
    # Hidden 1
    with tf.name_scope('hidden1'):
        '''
        A Variable is a modifiable tensor that lives in TensorFlow's graph of interacting operations.
        It can be used and even modified by the computation. For machine learning applications,
        one generally has the model parameters be Variables.
        '''  
        weights = tf.Variable(
            tf.truncated_normal([IMAGE_PIXELS, hidden1_units], stddev=1.0 / np.sqrt(float(IMAGE_PIXELS))), name='weights')
        biases = tf.Variable(tf.zeros([hidden1_units]), name='biases')
        hidden1 = tf.nn.relu(tf.matmul(images, weights) + biases)
    # Linear
    with tf.name_scope('softmax_linear'):
        weights = tf.Variable(
            tf.truncated_normal([hidden1_units, NUM_CLASSES], stddev=1.0 / np.sqrt(float(hidden1_units))), name='weights')
        biases = tf.Variable(tf.zeros([NUM_CLASSES]), name='biases')
        logits = tf.matmul(hidden1, weights) + biases
        
        return logits

def get_inference(images_ph, dropout_keep_prob_ph):
    #subtract average image
    with tf.variable_scope('centering') as scope:
        mean = tf.constant(vgg.average_image, dtype=tf.float32, name='avg_image')
        images_ph = tf.sub(images_ph, mean, name='subtract_avg')

    #get layers from vgg19
    vgg_layers = vgg.get_VGG_layers(images_ph, dropout_keep_prob_ph, train_fc_layers=True)

    #################################################
    ### Add more layers for semantic segmentation ###
    #################################################

    # convolution on top of pool4 to 21 chammenls (to make coarse predictions)
    with tf.variable_scope('conv9') as scope:
        conv9 = conv_layer(vgg_layers['pool4'], 21, 1, 'conv9')

    # convolution on top of conv7 (fc7) to 21 chammenls (to make coarse predictions)
    with tf.variable_scope('conv8') as scope:
        conv8 = conv_layer(vgg_layers['dropout2'], 21, 1, 'conv8')

    # 2x upsampling from last layer
    with tf.variable_scope('deconv1') as scope:
        shape = tf.shape(conv8)
        out_shape = tf.pack([shape[0], shape[1]*2, shape[2]*2, 21])
        weights = tf.Variable(tf.truncated_normal(mean=MEAN, stddev=0.1, shape=(4, 4, 21, 21)), name='weights')
        deconv1 = tf.nn.conv2d_transpose( value=conv8,
                                          filter=weights,
                                          output_shape=out_shape,
                                          strides=(1, 2, 2, 1),
                                          padding='SAME',
                                          name='deconv1')

        # slice 2x upsampled tensor in the last layer to fit pool4
        shape = tf.shape(conv9)
        size = tf.pack([-1, shape[1], shape[2], -1])
        deconv1 = tf.slice(deconv1, begin=[0,0,0,0], size=size, name="deconv1_slice")

    # combine preductions from last layer and pool4
    with tf.variable_scope('combined_pred') as scope:
        combined_pred = tf.add(deconv1, conv9, name="combined_pred")

    # 16x upsampling
    with tf.variable_scope('deconv2') as scope:
        shape = tf.shape(combined_pred)
        out_shape = tf.pack([shape[0], shape[1]*16, shape[2]*16, 21])
        weights = tf.Variable(tf.truncated_normal(mean=MEAN, stddev=0.1, shape=(32, 32, 21, 21)), name='weights')
        deconv2 = tf.nn.conv2d_transpose(value=combined_pred,
                                          filter=weights,
                                          output_shape=out_shape,
                                          strides=(1, 16, 16, 1),
                                          padding='SAME',
                                          name='deconv2')

        # slice upsampled tensor to original shape
        orig_shape = tf.shape(images_ph)
        size = tf.pack([-1, orig_shape[1], orig_shape[2], -1])
        logits = tf.slice(deconv2, begin=[0,0,0,0], size=size, name='logits')

    return logits

def create_new_conv_layer(input_data, num_input_channels, num_filters, filter_shape, pool_shape, name):
    # setup the filter input shape for tf.nn.conv_2d
    conv_filt_shape = [filter_shape[0], filter_shape[1], num_input_channels, num_filters]

    # initialise weights and bias for the filter
    weights = tf.Variable(tf.truncated_normal(conv_filt_shape, stddev=0.03), name=name+'_W')
    bias = tf.Variable(tf.truncated_normal([num_filters]), name=name+'_b')

    # setup the convolutional layer operation
    out_layer = tf.nn.conv2d(input_data, weights, [1, 1, 1, 1], padding='SAME')

    # add the bias
    out_layer += bias

    # apply a ReLU non-linear activation
    out_layer = tf.nn.relu(out_layer)

    # now perform max pooling
    # ksize is the argument which defines the size of the max pooling window (i.e. the area over which the maximum is
    # calculated).  It must be 4D to match the convolution - in this case, for each image we want to use a 2 x 2 area
    # applied to each channel
    ksize = [1, pool_shape[0], pool_shape[1], 1]
    # strides defines how the max pooling area moves through the image - a stride of 2 in the x direction will lead to
    # max pooling areas starting at x=0, x=2, x=4 etc. through your image.  If the stride is 1, we will get max pooling
    # overlapping previous max pooling areas (and no reduction in the number of parameters).  In this case, we want
    # to do strides of 2 in the x and y directions.
    strides = [1, 2, 2, 1]
    out_layer = tf.nn.max_pool(out_layer, ksize=ksize, strides=strides, padding='SAME')

    return out_layer