TensorFlow: Reading and using data from CSV file

Question

I've tried the codes provided by Tensorflow here

I've also tried the solution provided by Nicolas, I encountered an error:

ValueError: Shape () must have rank at least 1

but I am incapable of manipulating the code such that I can grab the data and place it in train_X and train_Y variables.

I'm currently using hard coded data for variable train_X and train_Y .

My csv file contains 2 columns, Height & State of Charge(SoC), where height is a float value and SoC is a whole number (Int) starting from 0 with increment of 10 to a maximum of 100.

I want to grab the data from the columns and use it in a linear regression model, where Height is the Y value and SoC is the x value.

Here's my code:

filename_queue = tf.train.string_input_producer("battdata.csv")

reader = tf.TextLineReader()
key, value = reader.read(filename_queue)

# Default values, in case of empty columns. Also specifies the type of the
# decoded result.
record_defaults = [[1], [1]]
col1, col2= tf.decode_csv(
    value, record_defaults=record_defaults)
features = tf.stack([col1, col2])

with tf.Session() as sess:
  # Start populating the filename queue.
  coord = tf.train.Coordinator()
  threads = tf.train.start_queue_runners(coord=coord)

  for i in range(1200):
    # Retrieve a single instance:
    example, label = sess.run([features, col2])

  coord.request_stop()
  coord.join(threads)

I want to change use the csv data in this model:

# Parameters
learning_rate = 0.01
training_epochs = 1000
display_step = 50

# Training Data
train_X = numpy.asarray([3.3,4.4,5.5,6.71,6.93,4.168,9.779,6.182,7.59,2.167,
                         7.042,10.791,5.313,7.997,5.654,9.27,3.1])
train_Y = numpy.asarray([1.7,2.76,2.09,3.19,1.694,1.573,3.366,2.596,2.53,1.221,
                         2.827,3.465,1.65,2.904,2.42,2.94,1.3])
n_samples = train_X.shape[0]

# tf Graph Input
X = tf.placeholder("float")#Charge
Y = tf.placeholder("float")#Height

# Set model weights
W = tf.Variable(rng.randn(), name="weight")
b = tf.Variable(rng.randn(), name="bias")

# Construct a linear model
pred = tf.add(tf.multiply(X, W), b) # XW + b <- y = mx + b  where W is gradient, b is intercept

# Mean squared error
cost = tf.reduce_sum(tf.pow(pred-Y, 2))/(2*n_samples)
# Gradient descent
optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)

# Initializing the variables
init = tf.global_variables_initializer()

# Launch the graph
    with tf.Session() as sess:
        sess.run(init)

        # Fit all training data
        for epoch in range(training_epochs):
            for (x, y) in zip(train_X, train_Y):
                sess.run(optimizer, feed_dict={X: x, Y: y})

            #Display logs per epoch step
            if (epoch+1) % display_step == 0:
                c = sess.run(cost, feed_dict={X: train_X, Y:train_Y})
                print( "Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(c), \
                    "W=", sess.run(W), "b=", sess.run(b))

        print("Optimization Finished!")
        training_cost = sess.run(cost, feed_dict={X: train_X, Y: train_Y})
        print ("Training cost=", training_cost, "W=", sess.run(W), "b=", sess.run(b), '\n')

        #Graphic display
        plt.plot(train_X, train_Y, 'ro', label='Original data')
        plt.plot(train_X, sess.run(W) * train_X + sess.run(b), label='Fitted line')
        plt.legend()
        plt.show()

EDIT:

I've also tried the solution provided by Nicolas, I encountered an error:

ValueError: Shape () must have rank at least 1

I solved this issue by adding square brackets around my file name like so:

filename_queue = tf.train.string_input_producer(['battdata.csv'])

Answer 1

All you need to do is to replace your placeholder tensors by the op you get form the decode_csv method. This way whenever you will run the optimiser , the TensorFlow Graph will ask for a new row to be read from the file through the various Tensor dependencies:

optimiser =>
cost => pred => X
cost => Y

It would give something like that:

filename_queue = tf.train.string_input_producer("battdata.csv")

reader = tf.TextLineReader()
key, value = reader.read(filename_queue)

# Default values, in case of empty columns. Also specifies the type of the
# decoded result.
record_defaults = [[1.], [1]]
X, Y = tf.decode_csv(
    value, record_defaults=record_defaults)

# Set model weights
W = tf.Variable(rng.randn(), name="weight")
b = tf.Variable(rng.randn(), name="bias")

# Construct a linear model
pred = tf.add(tf.multiply(X, W), b) # XW + b <- y = mx + b  where W is gradient, b is intercept

# Mean squared error
cost = tf.reduce_sum(tf.pow(pred-Y, 2))/(2*n_samples)
# Gradient descent
optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)

# Initializing the variables
init = tf.global_variables_initializer()

with tf.Session() as sess:
  # Start populating the filename queue.
  coord = tf.train.Coordinator()
  threads = tf.train.start_queue_runners(coord=coord)

  # Fit all training data
  for epoch in range(training_epochs):
      _, cost_value = sess.run([optimizer, cost])

   [...] # The rest of your code

  coord.request_stop()
  coord.join(threads) 

Answer 2

I had the same problem and the problem was resolved like:

tf.train.string_input_producer(tf.train.match_filenames_once("medal.csv"))

Found this here: .TensorFlow From CSV to API