Tensorflow将张量数组转换为单张量

Question

I'm building a LSTM RNN with Tensorflow that performs pixel-wise classification (or, maybe a better way to put it is, pixel-wise prediction?)

Bear with me as I explain the title.

The network looks like the following drawing...

The idea goes like this... an input image of size (200,200) is the input into a LSTM RNN of size (200,200,200). Each sequence output from the LSTM tensor vector (the pink boxes in the LSTM RNN) is fed into a MLP, and then the MLP makes a single output prediction -- ergo pixel-wise prediction (you can see how one input pixel generates one output "pixel"

The code looks like this (not all of the code, just parts that are needed):

...
n_input_x = 200
n_input_y = 200

x = tf.placeholder("float", [None, n_input_x, n_input_y])
y = tf.placeholder("float", [None, n_input_x, n_input_y])

def RNN(x):
    x = tf.transpose(x, [1, 0, 2])
    x = tf.reshape(x, [-1, n_input_x])
    x = tf.split(0, n_steps, x)

    lstm_cell = rnn_cell.BasicLSTMCell(n_hidden, forget_bias=1.0, state_is_tuple=True)
    outputs, states = rnn.rnn(lstm_cell, x, dtype=tf.float32)

    output_matrix = []
    for i in xrange(200):
        temp_vector = []
        for j in xrange(200):
            lstm_vector = outputs[j]
            pixel_pred = multilayer_perceptron(lstm_vector, mlp_weights, mlp_biases)
            temp_vector.append(pixel_pred)
        output_matrix.append(temp_vector)
        print i

    return output_matrix

temp = RNN(x)
pred = tf.placeholder(temp, [None, n_input_x, n_input_y])
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred, y))
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)
...

I have confirmed that the output of RNN -- that is, what is stored in temp is a 200x200 array of <tf.Tensor 'Softmax_39999:0' shape=(?, 1) dtype=float32>

As you can see, I place temp in a tf.placeholder of the same shape ( None for the batch size ... or do I need this?)... and the program just exits as if it completed running. Ideally what I want to see when I debug and print pred is something like <tf.Tensor shape=(200,200)>

When I debug, the first time I execute pred = tf.placeholder(temp, [None, n_input_x, n_input_y]) I get TypeError: TypeErro...32>]].",) and then it returns and I try again, and it says Exception AttributeError: "'NoneType' object has no attribute 'path'" in <function _remove at 0x7f1ab77c26e0> ignored

EDIT I also now realize that I need to place the lines

lstm_cell = rnn_cell.BasicLSTMCell(n_hidden, forget_bias=1.0, state_is_tuple=True)
outputs, states = rnn.rnn(lstm_cell, x, dtype=tf.float32)

Inside the first loop so that new 2D LSTM RNN are generated, however I'm getting an error about variable reusing ValueError: Variable RNN/BasicLSTMCell/Linear/Matrix does not exist, disallowed. Did you mean to set reuse=None in VarScope? ValueError: Variable RNN/BasicLSTMCell/Linear/Matrix does not exist, disallowed. Did you mean to set reuse=None in VarScope?

So in other words, is it isn't auto incrementing the RNN tensor name?

Answer 1

A more convenient way to report shapes is with tf.shape(). In your case:

size1 = tf.shape(temp)
sess = tf.Session()
size1_fetched = sess.run(size1, feed_dict = your_feed_dict)

That way, the size1_fetched is something like you would get from NumPy. Moreover, also your sizes for that particular feed_dict are given. For example, your [None, 200, 200] Tensor would be [64, 200, 200]

Another question: why do you have the placeholder in between your flow-graph? Will you later on feed pre-defined images feature-maps?