Python tensorflow.zeros函数代码示例

def make_variable_dict(max_age, max_gender):
  # TODO(sibyl-toe9oF2e):  Figure out how to derive max_age & max_gender from
  # examples_dict.
  age_weights = tf.Variable(tf.zeros([max_age + 1], dtype=tf.float32))
  gender_weights = tf.Variable(tf.zeros([max_gender + 1], dtype=tf.float32))
  return dict(sparse_features_weights=[age_weights, gender_weights],
              dense_features_weights=[])

    def __init__(self, dim_image, n_words, dim_hidden, batch_size, n_lstm_steps, drop_out_rate, bias_init_vector=None):
        self.dim_image = dim_image
        self.n_words = n_words
        self.dim_hidden = dim_hidden
        self.batch_size = batch_size
        self.n_lstm_steps = n_lstm_steps
	self.drop_out_rate = drop_out_rate

        with tf.device("/cpu:0"):
        	self.Wemb = tf.Variable(tf.random_uniform([n_words, dim_hidden], -0.1, 0.1), name='Wemb')
        #self.Wemb_W = tf.Variable(tf.random_uniform([n_words, dim_hidden], -0.1, 0.1), name='Wemb_W')
        #self.Wemb_b = tf.Variable(tf.random_uniform([dim_hidden], -0.1, 0.1), name='Wemb_b')

        #self.lstm3 = rnn_cell.BasicLSTMCell(dim_hidden)
        self.lstm3 = rnn_cell.LSTMCell(self.dim_hidden,2*self.dim_hidden,use_peepholes = True)
	self.lstm3_dropout = rnn_cell.DropoutWrapper(self.lstm3,output_keep_prob=1 - self.drop_out_rate)

        self.encode_image_W = tf.Variable( tf.random_uniform([dim_image, dim_hidden], -0.1, 0.1), name='encode_image_W')
        self.encode_image_b = tf.Variable( tf.zeros([dim_hidden]), name='encode_image_b')
	self.embed_att_w = tf.Variable(tf.random_uniform([dim_hidden, 1], -0.1,0.1), name='embed_att_w')
        self.embed_att_Wa = tf.Variable(tf.random_uniform([dim_hidden, dim_hidden], -0.1,0.1), name='embed_att_Wa')
	self.embed_att_Ua = tf.Variable(tf.random_uniform([dim_hidden, dim_hidden],-0.1,0.1), name='embed_att_Ua')
	self.embed_att_ba = tf.Variable( tf.zeros([dim_hidden]), name='embed_att_ba')

        self.embed_word_W = tf.Variable(tf.random_uniform([dim_hidden, n_words], -0.1,0.1), name='embed_word_W')
        if bias_init_vector is not None:
            self.embed_word_b = tf.Variable(bias_init_vector.astype(np.float32), name='embed_word_b')
        else:
            self.embed_word_b = tf.Variable(tf.zeros([n_words]), name='embed_word_b')

        self.embed_nn_Wp = tf.Variable(tf.random_uniform([3*dim_hidden, dim_hidden], -0.1,0.1), name='embed_nn_Wp')
        self.embed_nn_bp = tf.Variable(tf.zeros([dim_hidden]), name='embed_nn_bp')

    def testBlockGRUToGRUCellSingleStep(self):
        with self.test_session(use_gpu=self._use_gpu, graph=tf.Graph()) as sess:
            batch_size = 4
            cell_size = 5
            input_size = 6

            seed = 1994
            initializer = tf.random_uniform_initializer(-0.01, 0.01, seed=seed)

            # Inputs
            x = tf.zeros([batch_size, input_size])
            h = tf.zeros([batch_size, cell_size])

            # Values for the inputs.
            x_value = np.random.rand(batch_size, input_size)
            h_value = np.random.rand(batch_size, cell_size)

            # Output from the basic GRU cell implementation.
            with tf.variable_scope("basic", initializer=initializer):
                output = tf.nn.rnn_cell.GRUCell(cell_size)(x, h)
                sess.run([tf.initialize_all_variables()])
                basic_res = sess.run([output], {x: x_value, h: h_value})

            # Output from the block GRU cell implementation.
            with tf.variable_scope("block", initializer=initializer):
                output = gru_ops.GRUBlockCell(cell_size)(x, h)
                sess.run([tf.initialize_all_variables()])
                block_res = sess.run([output], {x: x_value, h: h_value})

            self.assertEqual(len(block_res), len(basic_res))
            for block, basic in zip(block_res, basic_res):
                self.assertAllClose(block, basic)

    def __init__(self, dim_image, n_words, dim_hidden, batch_size, n_lstm_steps, drop_out_rate, bias_init_vector=None):
        self.dim_image = dim_image
        self.n_words = n_words
        self.dim_hidden = dim_hidden
        self.batch_size = batch_size
        self.n_lstm_steps = n_lstm_steps
        self.drop_out_rate = drop_out_rate


        with tf.device("/gpu:2"):
            self.Wemb = tf.Variable(tf.random_uniform([n_words, dim_hidden], -0.1, 0.1), name='Wemb')

#         self.lstm1 = rnn_cell.BasicLSTMCell(dim_hidden)
#         self.lstm2 = rnn_cell.BasicLSTMCell(dim_hidden)
        
        self.lstm1 = rnn_cell.LSTMCell(self.dim_hidden,self.dim_hidden,use_peepholes = True)
        self.lstm1_dropout = rnn_cell.DropoutWrapper(self.lstm1,output_keep_prob=1 - self.drop_out_rate)
        self.lstm2 = rnn_cell.LSTMCell(self.dim_hidden,self.dim_hidden,use_peepholes = True)
        self.lstm2_dropout = rnn_cell.DropoutWrapper(self.lstm2,output_keep_prob=1 - self.drop_out_rate)
        
        
        # W is Weight, b is Bias 
        self.encode_image_W = tf.Variable( tf.random_uniform([dim_image, dim_hidden], -0.1, 0.1), name='encode_image_W')
        self.encode_image_b = tf.Variable( tf.zeros([dim_hidden]), name='encode_image_b')

        self.embed_word_W = tf.Variable(tf.random_uniform([dim_hidden, n_words], -0.1,0.1), name='embed_word_W')
        if bias_init_vector is not None:
            self.embed_word_b = tf.Variable(bias_init_vector.astype(np.float32), name='embed_word_b')
        else:
            self.embed_word_b = tf.Variable(tf.zeros([n_words]), name='embed_word_b')

def get_idx_map(shape):
    """Get index map for a image.
    Args:
        shape: [B, T, H, W] or [B, H, W]
    Returns:
        idx: [B, T, H, W, 2], or [B, H, W, 2]
    """
    s = shape
    ndims = tf.shape(s)
    wdim = ndims - 1
    hdim = ndims - 2
    idx_shape = tf.concat(0, [s, tf.constant([1])])
    ones_h = tf.ones(hdim - 1, dtype='int32')
    ones_w = tf.ones(wdim - 1, dtype='int32')
    h_shape = tf.concat(0, [ones_h, tf.constant([-1]), tf.constant([1, 1])])
    w_shape = tf.concat(0, [ones_w, tf.constant([-1]), tf.constant([1])])

    idx_y = tf.zeros(idx_shape, dtype='float')
    idx_x = tf.zeros(idx_shape, dtype='float')

    h = tf.slice(s, ndims - 2, [1])
    w = tf.slice(s, ndims - 1, [1])
    idx_y += tf.reshape(tf.to_float(tf.range(h[0])), h_shape)
    idx_x += tf.reshape(tf.to_float(tf.range(w[0])), w_shape)
    idx = tf.concat(ndims[0], [idx_y, idx_x])

    return idx

def main():
    sess = tf.Session()

    # 2進数3ビットから10進数
    x = tf.placeholder(tf.float32, [None, 3])
    w = tf.Variable(tf.zeros([3, 8]))
    b = tf.Variable(tf.zeros([8]))
    y = tf.nn.softmax(tf.matmul(x, w) + b)

    y_ = tf.placeholder(tf.float32, [None, 8])
    cross_entropy = -tf.reduce_sum(y_ * tf.log(y))
    train_step = tf.train.GradientDescentOptimizer(0.05).minimize(cross_entropy)

    sess.run(tf.initialize_all_variables())

    for i in range(1000):
        train_step.run({x: [[0, 0, 0]], y_: [[1, 0, 0, 0, 0, 0, 0, 0]]}, session=sess)
        train_step.run({x: [[1, 0, 0]], y_: [[0, 1, 0, 0, 0, 0, 0, 0]]}, session=sess)
        train_step.run({x: [[0, 1, 0]], y_: [[0, 0, 1, 0, 0, 0, 0, 0]]}, session=sess)
        train_step.run({x: [[1, 1, 0]], y_: [[0, 0, 0, 1, 0, 0, 0, 0]]}, session=sess)
        train_step.run({x: [[0, 0, 1]], y_: [[0, 0, 0, 0, 1, 0, 0, 0]]}, session=sess)
        train_step.run({x: [[1, 0, 1]], y_: [[0, 0, 0, 0, 0, 1, 0, 0]]}, session=sess)
        train_step.run({x: [[0, 1, 1]], y_: [[0, 0, 0, 0, 0, 0, 1, 0]]}, session=sess)
        train_step.run({x: [[1, 1, 1]], y_: [[0, 0, 0, 0, 0, 0, 0, 1]]}, session=sess)

    ## 1に近い予測があってるか 平均
    #correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
    #accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

    print(sess.run(y, feed_dict={x: [[0, 0, 0]]}))
    print(sess.run(y, feed_dict={x: [[1, 0, 0]]}))
    print(sess.run(y, feed_dict={x: [[0, 1, 0]]}))
    print(sess.run(y, feed_dict={x: [[1, 1, 0]]}))
    print(sess.run(y, feed_dict={x: [[0, 0, 1]]}))
    return 0

  def testDiscretizedMixLogisticLoss(self):
    batch = 2
    height = 4
    width = 4
    channels = 3
    num_mixtures = 5
    logits = tf.concat(  # assign all probability mass to first component
        [tf.ones([batch, height, width, 1]) * 1e8,
         tf.zeros([batch, height, width, num_mixtures - 1])],
        axis=-1)
    locs = tf.random_uniform([batch, height, width, num_mixtures * 3],
                             minval=-.9, maxval=.9)
    log_scales = tf.random_uniform([batch, height, width, num_mixtures * 3],
                                   minval=-1., maxval=1.)
    coeffs = tf.atanh(tf.zeros([batch, height, width, num_mixtures * 3]))
    pred = tf.concat([logits, locs, log_scales, coeffs], axis=-1)

    # Test labels that don't satisfy edge cases where 8-bit value is 0 or 255.
    labels = tf.random_uniform([batch, height, width, channels],
                               minval=-.9, maxval=.9)
    locs_0 = locs[..., :3]
    log_scales_0 = log_scales[..., :3]
    centered_labels = labels - locs_0
    inv_stdv = tf.exp(-log_scales_0)
    plus_in = inv_stdv * (centered_labels + 1. / 255.)
    min_in = inv_stdv * (centered_labels - 1. / 255.)
    cdf_plus = tf.nn.sigmoid(plus_in)
    cdf_min = tf.nn.sigmoid(min_in)
    expected_loss = -tf.reduce_sum(tf.log(cdf_plus - cdf_min), axis=-1)

    actual_loss = common_layers.discretized_mix_logistic_loss(
        pred=pred, labels=labels)
    actual_loss_val, expected_loss_val = self.evaluate(
        [actual_loss, expected_loss])
    self.assertAllClose(actual_loss_val, expected_loss_val, rtol=1e-5)

def inference(images,hidden1_units,hidden2_units):
    """建立前馈神经网络模型
    Args:
        images:输入图像数据
        hidden1_units:第一个隐藏层的神经元数目
        hidden2_units:第二个隐藏层 的神经元数目
    returns:
        softmax_linear:输出张量为计算后的结果
    """
    #隐藏层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)

    #隐藏层2
    with tf.name_scope('hidden2'):
        weights = tf.Variable(tf.truncated_normal([hidden1_units,hidden2_units],stddev=1.0/math.sqrt(float(hidden1_units))),name='weights')
        biases = tf.Variable(tf.zeros([hidden2_units]),name='biases')
        hidden2 = tf.nn.relu(tf.matmul(hidden1,weights)+biases)
    #线性输出层
    with tf.name_scope('softmax_linear'):
        weights = tf.Variable(tf.truncated_normal([hidden2_units,NUM_CLASSES]),name='biases')
        biases = tf.Variable(tf.zeros([NUM_CLASSES]),name='biases')
        logits = tf.matmul(hidden2,weights) + biases
    return logits

 def testBasicLSTMCellWithStateTuple(self):
   with self.test_session() as sess:
     with tf.variable_scope("root", initializer=tf.constant_initializer(0.5)):
       x = tf.zeros([1, 2])
       m0 = tf.zeros([1, 4])
       m1 = tf.zeros([1, 4])
       cell = tf.nn.rnn_cell.MultiRNNCell(
           [tf.nn.rnn_cell.BasicLSTMCell(2)] * 2, state_is_tuple=True)
       g, (out_m0, out_m1) = cell(x, (m0, m1))
       sess.run([tf.initialize_all_variables()])
       res = sess.run([g, out_m0, out_m1],
                      {x.name: np.array([[1., 1.]]),
                       m0.name: 0.1 * np.ones([1, 4]),
                       m1.name: 0.1 * np.ones([1, 4])})
       self.assertEqual(len(res), 3)
       # The numbers in results were not calculated, this is just a smoke test.
       # Note, however, these values should match the original
       # version having state_is_tuple=False.
       self.assertAllClose(res[0], [[0.24024698, 0.24024698]])
       expected_mem0 = np.array([[0.68967271, 0.68967271,
                                  0.44848421, 0.44848421]])
       expected_mem1 = np.array([[0.39897051, 0.39897051,
                                  0.24024698, 0.24024698]])
       self.assertAllClose(res[1], expected_mem0)
       self.assertAllClose(res[2], expected_mem1)

  def testBasicLSTMCellStateTupleType(self):
    with self.test_session():
      with tf.variable_scope("root", initializer=tf.constant_initializer(0.5)):
        x = tf.zeros([1, 2])
        m0 = (tf.zeros([1, 2]),) * 2
        m1 = (tf.zeros([1, 2]),) * 2
        cell = tf.nn.rnn_cell.MultiRNNCell(
            [tf.nn.rnn_cell.BasicLSTMCell(2)] * 2,
            state_is_tuple=True)
        self.assertTrue(isinstance(cell.state_size, tuple))
        self.assertTrue(isinstance(cell.state_size[0],
                                   tf.nn.rnn_cell.LSTMStateTuple))
        self.assertTrue(isinstance(cell.state_size[1],
                                   tf.nn.rnn_cell.LSTMStateTuple))

        # Pass in regular tuples
        _, (out_m0, out_m1) = cell(x, (m0, m1))
        self.assertTrue(isinstance(out_m0,
                                   tf.nn.rnn_cell.LSTMStateTuple))
        self.assertTrue(isinstance(out_m1,
                                   tf.nn.rnn_cell.LSTMStateTuple))

        # Pass in LSTMStateTuples
        tf.get_variable_scope().reuse_variables()
        zero_state = cell.zero_state(1, tf.float32)
        self.assertTrue(isinstance(zero_state, tuple))
        self.assertTrue(isinstance(zero_state[0],
                                   tf.nn.rnn_cell.LSTMStateTuple))
        self.assertTrue(isinstance(zero_state[1],
                                   tf.nn.rnn_cell.LSTMStateTuple))
        _, (out_m0, out_m1) = cell(x, zero_state)
        self.assertTrue(
            isinstance(out_m0, tf.nn.rnn_cell.LSTMStateTuple))
        self.assertTrue(
            isinstance(out_m1, tf.nn.rnn_cell.LSTMStateTuple))

 def __init__(self, name, input_size, output_size):
     with tf.name_scope("rbm_" + name):
         self.weights = tf.Variable(
             tf.truncated_normal([input_size, output_size],
                 stddev=1.0 / math.sqrt(float(input_size))), name="weights")
         self.v_bias = tf.Variable(tf.zeros([input_size]), name="v_bias")
         self.h_bias = tf.Variable(tf.zeros([output_size]), name="h_bias")

  def testGradientsAsVariables(self):
    for dtype in [tf.half, tf.float32, tf.float64]:
      with self.test_session() as sess:
        var0 = tf.Variable([1.0, 2.0], dtype=dtype)
        var1 = tf.Variable([3.0, 4.0], dtype=dtype)
        cost = 5 * var0 + 3 * var1
        global_step = tf.Variable(tf.zeros([], tf.int64), name='global_step')
        sgd_op = tf.train.GradientDescentOptimizer(3.0)
        grads_and_vars = sgd_op.compute_gradients(cost, [var0, var1])
        # Convert gradients to tf.Variables
        converted_grads = [
            tf.Variable(tf.zeros([2], dtype)) for i in grads_and_vars
        ]
        convert_ops = [
            tf.assign(converted_grads[i], gv[0])
            for i, gv in enumerate(grads_and_vars)
        ]

        converted_grads_and_vars = list(zip(converted_grads, [var0, var1]))
        opt_op = sgd_op.apply_gradients(converted_grads_and_vars, global_step)

        tf.global_variables_initializer().run()
        # Run convert_ops to achieve the gradietns converting
        sess.run(convert_ops)
        # Fetch params to validate initial values
        self.assertAllClose([1.0, 2.0], var0.eval())
        self.assertAllClose([3.0, 4.0], var1.eval())
        # Run 1 step of sgd through optimizer
        opt_op.run()
        # Validate updated params
        self.assertAllClose([-14., -13.], var0.eval())
        self.assertAllClose([-6., -5.], var1.eval())

def model(images, inits, num_iterations=4, num_patches=68, patch_shape=(24, 24), num_channels=3):
  batch_size = images.get_shape().as_list()[0]
  hidden_state = tf.zeros((batch_size, 512))
  dx = tf.zeros((batch_size, num_patches, 2))
  endpoints = {}
  dxs = []

  for step in range(num_iterations):
      with tf.device('/cpu:0'):
          patches = tf.image.extract_patches(images, tf.constant(patch_shape), inits+dx)
      patches = tf.reshape(patches, (batch_size * num_patches, patch_shape[0], patch_shape[1], num_channels))

      endpoints['patches'] = patches
      with tf.variable_scope('convnet', reuse=step>0):
          net = conv_model(patches)
          ims = net['concat']

      ims = tf.reshape(ims, (batch_size, -1))

      with tf.variable_scope('rnn', reuse=step>0) as scope:
          hidden_state = slim.ops.fc(tf.concat(1, [ims, hidden_state]), 512, activation=tf.tanh)
          prediction = slim.ops.fc(hidden_state, num_patches * 2, scope='pred', activation=None)
          endpoints['prediction'] = prediction
      prediction = tf.reshape(prediction, (batch_size, num_patches, 2))
      dx += prediction
      dxs.append(dx)

  return inits + dx, dxs, endpoints

  def testCompatibleNames(self):
    with self.test_session(use_gpu=self._use_gpu, graph=tf.Graph()):
      cell = tf.nn.rnn_cell.LSTMCell(10)
      pcell = tf.nn.rnn_cell.LSTMCell(10, use_peepholes=True)
      inputs = [tf.zeros([4, 5])] * 6
      tf.nn.rnn(cell, inputs, dtype=tf.float32, scope="basic")
      tf.nn.rnn(pcell, inputs, dtype=tf.float32, scope="peephole")
      basic_names = {v.name: v.get_shape() for v in tf.trainable_variables()}

    with self.test_session(use_gpu=self._use_gpu, graph=tf.Graph()):
      cell = tf.contrib.rnn.LSTMBlockCell(10, use_compatible_names=True)
      pcell = tf.contrib.rnn.LSTMBlockCell(
          10, use_peephole=True, use_compatible_names=True)
      inputs = [tf.zeros([4, 5])] * 6
      tf.nn.rnn(cell, inputs, dtype=tf.float32, scope="basic")
      tf.nn.rnn(pcell, inputs, dtype=tf.float32, scope="peephole")
      block_names = {v.name: v.get_shape() for v in tf.trainable_variables()}

    with self.test_session(use_gpu=self._use_gpu, graph=tf.Graph()):
      cell = tf.contrib.rnn.LSTMBlockFusedCell(10)
      pcell = tf.contrib.rnn.LSTMBlockFusedCell(10, use_peephole=True)
      inputs = [tf.zeros([4, 5])] * 6
      cell(inputs, dtype=tf.float32, scope="basic/LSTMCell")
      pcell(inputs, dtype=tf.float32, scope="peephole/LSTMCell")
      fused_names = {v.name: v.get_shape() for v in tf.trainable_variables()}

    self.assertEqual(basic_names, block_names)
    self.assertEqual(basic_names, fused_names)

def autoencoder_contd(input_dim, representation):
	x = tf.placeholder(tf.float32, [None, input_dim]);
	high_decW=tf.Variable(
		initial_value=tf.random_normal(
			[representation,input_dim],
			-math.sqrt(6.0/(input_dim+representation)),
			math.sqrt(6.0/(input_dim+representation))),
		dtype=tf.float32,
		name='high_decW');
	# high_encW=tf.transpose(high_decW);
	high_encW=tf.Variable(
		initial_value=tf.random_normal(
			[input_dim, representation],
			-math.sqrt(6.0/(input_dim+representation)),
			math.sqrt(6.0/(input_dim+representation))),
		name='high_encW');
	high_encb=tf.Variable(tf.zeros([representation]),
		name='high_encb');
	z=tf.nn.sigmoid(tf.matmul(x,high_encW) + high_encb);
	hidden_weights=high_encW;
	
	high_decb=tf.Variable(
		tf.zeros([input_dim]),
		name='high_decb');
	y=tf.nn.sigmoid(tf.matmul(z,high_decW)+high_decb);
	cost=tf.nn.l2_loss(x-y);
	loss_per_pixel=tf.reduce_mean(tf.abs(x-y));
	return {'x':x,'z':z,'y':y,'cost':cost,
		'weights':hidden_weights,
		'encW':high_encW,'decW':high_decW,
		'encb':high_encb,'decb':high_decb,
		'ppx':loss_per_pixel
		};