Convert a list of images and labels to np array to train tensorflow

Convert a list of images and labels to np array to train tensorflow

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Convert a list of images and labels to np array to train tensorflow
Tag : python , By : John Q.
Date : November 26 2020, 03:01 PM

around this issue X_train looks like a list of numpy arrays and tensorflow expects a numpy array, you can simply convert it to a numpy array by:
X_train = np.array(X_train)
X_train = np.asarray(X_train)

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How to train images in CNN with Tensorflow

Tag : tensorflow , By : user186012
Date : March 29 2020, 07:55 AM
I wish this help you I am a beginner of TensorFlow, and I am trying to build to CNN model. Here is the sample code I refer to: https://github.com/MorvanZhou/tutorials/blob/master/tensorflowTUT/tf18_CNN3/full_code.py
    image_batch = tf.train.batch([resized_image], batch_size=100)
images, label_batch = tf.train.batch(
    [image, label],
    capacity=min_queue_examples + 3 * batch_size)

how do i make labels list manually for my imported images in tensorflow

Tag : tensorflow , By : Alex Bartzas
Date : March 29 2020, 07:55 AM
wish help you to fix your issue , If you know you want labels = [1, 1, 1, 0, 0], just use
tf.one_hot(labels, depth=2)
array([[ 0.,  1.],
       [ 0.,  1.],
       [ 0.,  1.],
       [ 1.,  0.],
       [ 1.,  0.]], dtype=float32)
<tf.Tensor 'one_hot:0' shape=(5, 2) dtype=float32>
labels = tf.placeholder(tf.int32, [None])  # 'None' means it has one dimension that is determined by your batch size
# ... define your network ...
loss_op = tf.nn.softmax_cross_entropy_with_logits(logits=logits,
loss_op = tf.reduce_mean(loss_op)
for _ in range(num_iter):
    d = # generate data batch
    t = # generate label batch, e.g. [1, 1] for the first two images
    _, batch_loss = sess.run([train_op, loss_op],
                             feed_dict={data: d, labels: t})

Tensorflow: batching labels with tf.train.batch

Tag : tensorflow , By : Denis Chaykovskiy
Date : March 29 2020, 07:55 AM
fixed the issue. Will look into that further According to the Tensorflow documentation of tf.train.batch (https://www.tensorflow.org/api_docs/python/tf/train/batch),

Tensorflow - use string labels to train neural network

Tag : python , By : TC.
Date : March 29 2020, 07:55 AM
I wish did fix the issue. Finally I've found the error. Because I'm quite new to machine learning I've forgot that many algorithms does not handle categorical datasets.
The solution has been to perform a one-hot encoding on the target labels and feed this new array to the newtork with this function:
# define universe of possible input values
alphabet = 'abcdefghijklmnopqrstuvwxyz'

# define a mapping of chars to integers
char_to_int = dict((c, i) for i, c in enumerate(alphabet))
int_to_char = dict((i, c) for i, c in enumerate(alphabet))

def one_hot_encode(data_array):
    integer_encoded = [char_to_int[char] for char in data_array]

    # one hot encode
    onehot_encoded = list()
    for value in integer_encoded:
        letter = [0 for _ in range(len(alphabet))]
        letter[value] = 1

    return onehot_encoded

Convert Tensorflow Dataset into 2 arrays containing images and labels

Tag : python , By : DarrenBeck
Date : March 29 2020, 07:55 AM
should help you out If I understand your question well, what you need now to do is to concatenate to a numpy array as you iterate through your dataset. Note that, during iteration, if you apply .numpy() operation, you automatically convert from tf.tensor to np.array.
Therefore, the following options are available to you:
  a = np.array([[1, 2], [3, 4]])
  b = np.array([[5, 6]])
  np.concatenate((a, b), axis=0)
array([[1, 2],
       [3, 4],
       [5, 6]])
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