Classify MNIST Digits with Tensorflow by a 2-layer RNN approach. Training works fine, but when evaluating accuracy, incompatible shape of test data is reported.
import tensorflow as tf
import inspect
import numpy as np
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("/tmp/data/", one_hot = True)
hm_epochs = 1
n_classes = 10
batch_size = 128
chunk_size = 28
n_chunks = 28
rnn_size = 128
x = tf.placeholder('float', [None, n_chunks,chunk_size])
y = tf.placeholder('float')
def lstm_cell():
if 'reuse' in inspect.getargspec(
tf.contrib.rnn.BasicLSTMCell.__init__).args:
return tf.contrib.rnn.BasicLSTMCell(
rnn_size, forget_bias=0.0, state_is_tuple=True,
reuse=tf.get_variable_scope().reuse)
else:
return tf.contrib.rnn.BasicLSTMCell(
rnn_size, forget_bias=0.0, state_is_tuple=True)
def attn_cell():
return tf.contrib.rnn.DropoutWrapper(
lstm_cell())
def recurrent_neural_network(x):
layer = {'weights':tf.Variable(tf.random_normal([rnn_size,n_classes])),
'biases':tf.Variable(tf.random_normal([n_classes]))}
x = tf.transpose(x, [1,0,2])
x = tf.reshape(x, [-1, chunk_size])
x = tf.split(x, n_chunks, 0)
stacked_lstm = tf.contrib.rnn.MultiRNNCell([attn_cell(),attn_cell()], state_is_tuple=True)
initial_state = state = stacked_lstm.zero_state(batch_size, tf.float32)
outputs, states = tf.contrib.rnn.static_rnn(stacked_lstm, x,state)
output = tf.matmul(outputs[-1],layer['weights']) + layer['biases']
return output
def train_neural_network(x):
prediction = recurrent_neural_network(x)
cost = tf.reduce_mean( tf.nn.softmax_cross_entropy_with_logits(labels=y,logits=prediction) )
optimizer = tf.train.AdamOptimizer().minimize(cost)
with tf.Session() as sess:
sess.run(tf.initialize_all_variables())
for epoch in range(hm_epochs):
epoch_loss = 0
for _ in range(int(mnist.train.num_examples/batch_size)):
epoch_x, epoch_y = mnist.train.next_batch(batch_size)
epoch_x = epoch_x.reshape((batch_size,n_chunks,chunk_size))
_, c = sess.run([optimizer, cost], feed_dict={x: epoch_x, y: epoch_y})
epoch_loss += c
print('Epoch', epoch, 'completed out of',hm_epochs,'loss:',epoch_loss)
correct = tf.equal(tf.argmax(prediction, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct, 'float'))
testdata= np.reshape( mnist.test.images, (10000, n_chunks, chunk_size))
print("Testdata ",testdata.shape)
print("x ",x)
print('Accuracy:',accuracy.eval({x:testdata, y:mnist.test.labels}))
train_neural_network(x)
However, the shapes of test data and placeholders are printed as follows. Aren't they compatible?
Epoch 0 completed out of 1 loss: 228.159379691
Testdata (10000, 28, 28)
x Tensor("Placeholder:0", shape=(?, 28, 28), dtype=float32)
Error:
Caused by op 'rnn/rnn/multi_rnn_cell/cell_0/cell_0/basic_lstm_cell/basic_lstm_ce
ll/concat', defined at:
File "main.py", line 90, in <module>
train_neural_network(x)
File "main.py", line 59, in train_neural_network
prediction = recurrent_neural_network(x)
File "main.py", line 52, in recurrent_neural_network
outputs, states = tf.contrib.rnn.static_rnn(stacked_lstm, x,state)
File "C:\Users\henry\Anaconda3\lib\site-packages\tensorflow\python\ops\rnn.py"
, line 1212, in static_rnn
(output, state) = call_cell()
File "C:\Users\henry\Anaconda3\lib\site-packages\tensorflow\python\ops\rnn.py"
, line 1199, in <lambda>
call_cell = lambda: cell(input_, state)
File "C:\Users\henry\Anaconda3\lib\site-packages\tensorflow\python\ops\rnn_cel
l_impl.py", line 180, in __call__
return super(RNNCell, self).__call__(inputs, state)
File "C:\Users\henry\Anaconda3\lib\site-packages\tensorflow\python\layers\base
.py", line 441, in __call__
outputs = self.call(inputs, *args, **kwargs)
File "C:\Users\henry\Anaconda3\lib\site-packages\tensorflow\python\ops\rnn_cel
l_impl.py", line 916, in call
cur_inp, new_state = cell(cur_inp, cur_state)
File "C:\Users\henry\Anaconda3\lib\site-packages\tensorflow\python\ops\rnn_cel
l_impl.py", line 752, in __call__
output, new_state = self._cell(inputs, state, scope)
File "C:\Users\henry\Anaconda3\lib\site-packages\tensorflow\python\ops\rnn_cel
l_impl.py", line 180, in __call__
return super(RNNCell, self).__call__(inputs, state)
File "C:\Users\henry\Anaconda3\lib\site-packages\tensorflow\python\layers\base
.py", line 441, in __call__
outputs = self.call(inputs, *args, **kwargs)
File "C:\Users\henry\Anaconda3\lib\site-packages\tensorflow\python\ops\rnn_cel
l_impl.py", line 383, in call
concat = _linear([inputs, h], 4 * self._num_units, True)
File "C:\Users\henry\Anaconda3\lib\site-packages\tensorflow\python\ops\rnn_cel
l_impl.py", line 1021, in _linear
res = math_ops.matmul(array_ops.concat(args, 1), weights)
File "C:\Users\henry\Anaconda3\lib\site-packages\tensorflow\python\ops\array_o
ps.py", line 1048, in concat
name=name)
File "C:\Users\henry\Anaconda3\lib\site-packages\tensorflow\python\ops\gen_arr
ay_ops.py", line 495, in _concat_v2
name=name)
File "C:\Users\henry\Anaconda3\lib\site-packages\tensorflow\python\framework\o
p_def_library.py", line 767, in apply_op
op_def=op_def)
File "C:\Users\henry\Anaconda3\lib\site-packages\tensorflow\python\framework\o
ps.py", line 2506, in create_op
original_op=self._default_original_op, op_def=op_def)
File "C:\Users\henry\Anaconda3\lib\site-packages\tensorflow\python\framework\o
ps.py", line 1269, in __init__
self._traceback = _extract_stack()
InvalidArgumentError (see above for traceback): ConcatOp : Dimensions of inputs
should match: shape[0] = [10000,28] vs. shape[1] = [128,128]
[[Node: rnn/rnn/multi_rnn_cell/cell_0/cell_0/basic_lstm_cell/basic_lstm
_cell/concat = ConcatV2[N=2, T=DT_FLOAT, Tidx=DT_INT32, _device="/job:localhost/
replica:0/task:0/cpu:0"](split, MultiRNNCellZeroState/DropoutWrapperZeroState/Ba
sicLSTMCellZeroState/zeros_1, rnn/rnn/multi_rnn_cell/cell_0/cell_0/basic_lstm_ce
ll/basic_lstm_cell/concat/axis)]]
When I print the shape of training data it is (128,28,28). I am confused that why the test data leads to the error because both training data and test data are formatted in the same way, that is (?,n_chunks,chunk_size). Thanks in advance.
The issue is that you always create the initial state with shape set to the training batch size instead of the eval batch size.
This is the culprit line:
initial_state = state = stacked_lstm.zero_state(batch_size, tf.float32)
Related
I used to use this code to train variational autoencoder (I found the code on a forum and adapted it to my needs) :
import pickle
from pylab import mpl,plt
#lecture des résultats
filename=r'XXX.pic'
data_file=open(filename,'rb')
X_sec = pickle.load(data_file)#[:,3000:]
data_file.close()
size=X_sec.shape[0]
prop=0.75
cut=int(size*prop)
X_train=X_sec[:cut]
X_test=X_sec[cut:]
std=X_train.std()
X_train /= std
X_test /= std
import keras
from keras import layers
from keras import backend as K
from keras.models import Model
import numpy as np
#encoding_dim = 12
sig_shape = (3600,)
batch_size = 128
latent_dim = 12
input_sig = keras.Input(shape=sig_shape)
x = layers.Dense(128, activation='relu')(input_sig)
x = layers.Dense(64, activation='relu')(x)
shape_before_flattening = K.int_shape(x)
x = layers.Dense(32, activation='relu')(x)
z_mean = layers.Dense(latent_dim)(x)
z_log_var = layers.Dense(latent_dim)(x)
encoder=Model(input_sig,[z_mean,z_log_var])
def sampling(args):
z_mean, z_log_var = args
epsilon = K.random_normal(shape=(K.shape(z_mean)[0], latent_dim),
mean=0., stddev=1.)
return z_mean + K.exp(z_log_var) * epsilon
z = layers.Lambda(sampling)([z_mean, z_log_var])
decoder_input = layers.Input(K.int_shape(z)[1:])
x = layers.Dense(np.prod(shape_before_flattening[1:]),activation='relu')(decoder_input)
x = layers.Reshape(shape_before_flattening[1:])(x)
x = layers.Dense(128, activation='relu')(x)
x = layers.Dense(3600, activation='linear')(x)
decoder = Model(decoder_input, x)
z_decoded = decoder(z)
class CustomVariationalLayer(keras.layers.Layer):
def vae_loss(self, x, z_decoded):
x = K.flatten(x)
z_decoded = K.flatten(z_decoded)
xent_loss = keras.metrics.mae(x, z_decoded)
kl_loss = -5e-4 * K.mean(
1 + z_log_var - K.square(z_mean) - K.exp(z_log_var), axis=-1)
return K.mean(xent_loss + kl_loss)
def call(self, inputs):
x = inputs[0]
z_decoded = inputs[1]
loss = self.vae_loss(x, z_decoded)
self.add_loss(loss, inputs=inputs)
return x
y = CustomVariationalLayer()([input_sig, z_decoded])
vae = Model(input_sig, y)
vae.compile(optimizer='rmsprop', loss=None)
vae.summary()
vae.fit(x=X_train, y=None,shuffle=True,epochs=100,batch_size=batch_size,validation_data=(X_test, None))
it used to work smoothly but I have updated my librairies and now I get this error :
File
"C:\Users\user\AppData\Local\conda\conda\envs\my_root\lib\site-packages\tensorflow_core\python\framework\ops.py",
line 1619, in _create_c_op
c_op = c_api.TF_FinishOperation(op_desc)
InvalidArgumentError: Duplicate node name in graph:
'lambda_1/random_normal/shape'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File
"I:\Documents\Nico\Python\finance\travail_amont\autoencoder_variationnel_bruit.py",
line 74, in
z = layers.Lambda(sampling)([z_mean, z_log_var])
File
"C:\Users\user\AppData\Local\conda\conda\envs\my_root\lib\site-packages\keras\backend\tensorflow_backend.py",
line 75, in symbolic_fn_wrapper
return func(*args, **kwargs)
File
"C:\Users\user\AppData\Local\conda\conda\envs\my_root\lib\site-packages\keras\engine\base_layer.py",
line 506, in call
output_shape = self.compute_output_shape(input_shape)
File
"C:\Users\user\AppData\Local\conda\conda\envs\my_root\lib\site-packages\keras\layers\core.py",
line 674, in compute_output_shape
x = self.call(xs)
File
"C:\Users\user\AppData\Local\conda\conda\envs\my_root\lib\site-packages\keras\layers\core.py",
line 716, in call
return self.function(inputs, **arguments)
File
"I:\Documents\Nico\Python\finance\travail_amont\autoencoder_variationnel_bruit.py",
line 71, in sampling
mean=0., stddev=1.)
File
"C:\Users\user\AppData\Local\conda\conda\envs\my_root\lib\site-packages\keras\backend\tensorflow_backend.py",
line 4329, in random_normal
shape, mean=mean, stddev=stddev, dtype=dtype, seed=seed)
File
"C:\Users\user\AppData\Local\conda\conda\envs\my_root\lib\site-packages\tensorflow_core\python\keras\backend.py",
line 5602, in random_normal
shape, mean=mean, stddev=stddev, dtype=dtype, seed=seed)
File
"C:\Users\user\AppData\Local\conda\conda\envs\my_root\lib\site-packages\tensorflow_core\python\ops\random_ops.py",
line 69, in random_normal
shape_tensor = tensor_util.shape_tensor(shape)
File
"C:\Users\user\AppData\Local\conda\conda\envs\my_root\lib\site-packages\tensorflow_core\python\framework\tensor_util.py",
line 994, in shape_tensor
return ops.convert_to_tensor(shape, dtype=dtype, name="shape")
File
"C:\Users\user\AppData\Local\conda\conda\envs\my_root\lib\site-packages\tensorflow_core\python\framework\ops.py",
line 1314, in convert_to_tensor
ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
File
"C:\Users\user\AppData\Local\conda\conda\envs\my_root\lib\site-packages\tensorflow_core\python\ops\array_ops.py",
line 1368, in _autopacking_conversion_function
return _autopacking_helper(v, dtype, name or "packed")
File
"C:\Users\user\AppData\Local\conda\conda\envs\my_root\lib\site-packages\tensorflow_core\python\ops\array_ops.py",
line 1304, in _autopacking_helper
return gen_array_ops.pack(elems_as_tensors, name=scope)
File
"C:\Users\user\AppData\Local\conda\conda\envs\my_root\lib\site-packages\tensorflow_core\python\ops\gen_array_ops.py",
line 5704, in pack
"Pack", values=values, axis=axis, name=name)
File
"C:\Users\user\AppData\Local\conda\conda\envs\my_root\lib\site-packages\tensorflow_core\python\framework\op_def_library.py",
line 742, in _apply_op_helper
attrs=attr_protos, op_def=op_def)
File
"C:\Users\user\AppData\Local\conda\conda\envs\my_root\lib\site-packages\tensorflow_core\python\framework\func_graph.py",
line 595, in _create_op_internal
compute_device)
File
"C:\Users\user\AppData\Local\conda\conda\envs\my_root\lib\site-packages\tensorflow_core\python\framework\ops.py",
line 3322, in _create_op_internal
op_def=op_def)
File
"C:\Users\user\AppData\Local\conda\conda\envs\my_root\lib\site-packages\tensorflow_core\python\framework\ops.py",
line 1786, in init
control_input_ops)
File
"C:\Users\user\AppData\Local\conda\conda\envs\my_root\lib\site-packages\tensorflow_core\python\framework\ops.py",
line 1622, in _create_c_op
raise ValueError(str(e))
ValueError: Duplicate node name in graph:
'lambda_1/random_normal/shape'
I do not know this error : "Duplicate node name in graph". Does anyone has a clue ? Thanks.
If you're using tf 2.x, then import your keras modules as follows.
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.kerasimport backend as K
from tensorflow.keras.models import Model
More related on this, #36509, #130
I don't know why I am getting this error.
I saw a some posts to change state_is_tuple=False but it was giving me some other error. I think the error is in the way I defined lstm cell but not sure what should I change? I followed this link which has similar code structure.
Here is my code:
Required placeholders
n_hidden = args.rnn_size
n_layers = args.num_layers
max_sequence_length = args.max_sequence_length
encoderEmbeddingsize = args.encoderEmbeddingsize
decoderEmbeddingsize = args.decoderEmbeddingsize
queVocabsize = len(question_vocab_to_int)
ansVocabsize = len(answer_vocab_to_int)
batch_size = args.batch_size
# Input Embedding for Encoder ## CHECK THE VOCAB SIZE!!!
encoder_input = tf.contrib.layers.embed_sequence(input_data, queVocabsize, encoderEmbeddingsize,
initializer=tf.random_uniform_initializer(0, 1))
print('encoder_input', encoder_input)
# Layers for the model
lstm_cell = rnn.BasicLSTMCell(n_hidden) # lstm layer
dropout = rnn.DropoutWrapper(lstm_cell, input_keep_prob=keep_prob) # dropout layer
# Encoder Model
# Make two layer encoder
encoder_multirnn_cell = rnn.MultiRNNCell([dropout]*n_layers)
# Make it bidirectional
print(sequence_length)
encoder_output, encoder_state = tf.nn.dynamic_rnn(encoder_multirnn_cell,
inputs=encoder_input, dtype=tf.float32) # sequence_length=sequence_length,
print('encoder_output', encoder_output)
print('encoder_state', encoder_state)
# preprocessing encoder input
initial_tensor = tf.strided_slice(target, [0, 0], [batch_size, -1], [1, 1])
decoder_input = tf.concat([tf.fill([batch_size, 1], question_vocab_to_int['<GO>']), initial_tensor], 1)
print('decoder_input', decoder_input)
## Input Embedding for the Decoder
decoder_embedding = tf.Variable(tf.random_uniform([queVocabsize+1, decoderEmbeddingsize], 0, 1))
decoder_embedded_input = tf.nn.embedding_lookup(decoder_embedding, decoder_input)
print('check')
print(decoder_embedded_input)
print(decoder_embedding)
## Decoder Model
#with tf.variable_scope("decoding") as decoding_scope:
lstm_decoder_cell = rnn.BasicLSTMCell(n_hidden) # lstm layer
dropout_decoder = rnn.DropoutWrapper(lstm_decoder_cell, input_keep_prob=keep_prob) # droput layer
# decoder
# Make two layer encoder
decoder_multirnn_cell = rnn.MultiRNNCell([dropout_decoder] * n_layers)
# weights = tf.truncated_normal_initializer(stddev=0.1)
# biases = tf.zeros_initializer()
output_layer_function = layers_core.Dense(
ansVocabsize, use_bias=False) #lambda x: tf.contrib.layers.fully_connected(x, queVocabsize, scope=decoding_scope,
# weights_initializer=weights,
# biases_initializer=biases)
#print(decoder_multirnn_cell.output_size)
#decoding_scope.reuse_variables()
print('output_kayer_function', output_layer_function)
# training vs inference!
encoder_output = tf.transpose(encoder_output, [1, 0, 2])
attention_state = tf.zeros([batch_size, 1, decoder_multirnn_cell.output_size * 2])
attention_mechanism = tf.contrib.seq2seq.BahdanauAttention(
num_units=decoder_multirnn_cell.output_size, memory=encoder_output)
lstm_decoder_cell = tf.contrib.seq2seq.AttentionWrapper(lstm_decoder_cell,
attention_mechanism=attention_mechanism)
attn_zero = lstm_decoder_cell.zero_state(batch_size=batch_size, dtype=tf.float32)
init_state = attn_zero.clone(cell_state=encoder_state)
print(('sequence!!!!!!!!1', sequence_length))
helper = tf.contrib.seq2seq.TrainingHelper(decoder_embedded_input, sequence_length)
# decoder
decoder = tf.contrib.seq2seq.BasicDecoder(lstm_decoder_cell, helper, initial_state=init_state,
output_layer= output_layer_function)
print(decoder)
final_outputs, _final_state, _final_sequence_lengths = tf.contrib.seq2seq.dynamic_decode(decoder)
train_pred_drop = tf.nn.dropout(final_outputs, keep_prob)
logits = train_pred_drop.rnn_output
Now, I am getting the error in tf.contrib.seq2seq.dynamic_decode(decoder), as shown below:
Traceback (most recent call last):
File "test_model.py", line 272, in <module>
train_logits, infer_logits = load_model(args, tf.reverse(input_data, [-1]), target, learning_rate, sequence_length, question_vocab_to_int, answer_vocab_to_int, keep_prob ) ## load model here!
File "test_model.py", line 165, in load_model
final_outputs, _final_state, _final_sequence_lengths = tf.contrib.seq2seq.dynamic_decode(decoder)
File "/home/saurabh/tfnightly/lib/python3.5/site-packages/tensorflow/contrib/seq2seq/python/ops/decoder.py", line 286, in dynamic_decode
swap_memory=swap_memory)
File "/home/saurabh/tfnightly/lib/python3.5/site-packages/tensorflow/python/ops/control_flow_ops.py", line 2816, in while_loop
result = loop_context.BuildLoop(cond, body, loop_vars, shape_invariants)
File "/home/saurabh/tfnightly/lib/python3.5/site-packages/tensorflow/python/ops/control_flow_ops.py", line 2640, in BuildLoop
pred, body, original_loop_vars, loop_vars, shape_invariants)
File "/home/saurabh/tfnightly/lib/python3.5/site-packages/tensorflow/python/ops/control_flow_ops.py", line 2590, in _BuildLoop
body_result = body(*packed_vars_for_body)
File "/home/saurabh/tfnightly/lib/python3.5/site-packages/tensorflow/contrib/seq2seq/python/ops/decoder.py", line 234, in body
decoder_finished) = decoder.step(time, inputs, state)
File "/home/saurabh/tfnightly/lib/python3.5/site-packages/tensorflow/contrib/seq2seq/python/ops/basic_decoder.py", line 138, in step
cell_outputs, cell_state = self._cell(inputs, state)
File "/home/saurabh/tfnightly/lib/python3.5/site-packages/tensorflow/python/ops/rnn_cell_impl.py", line 183, in __call__
return super(RNNCell, self).__call__(inputs, state)
File "/home/saurabh/tfnightly/lib/python3.5/site-packages/tensorflow/python/layers/base.py", line 575, in __call__
outputs = self.call(inputs, *args, **kwargs)
File "/home/saurabh/tfnightly/lib/python3.5/site-packages/tensorflow/contrib/seq2seq/python/ops/attention_wrapper.py", line 1295, in call
cell_output, next_cell_state = self._cell(cell_inputs, cell_state)
File "/home/saurabh/tfnightly/lib/python3.5/site-packages/tensorflow/python/ops/rnn_cell_impl.py", line 183, in __call__
return super(RNNCell, self).__call__(inputs, state)
File "/home/saurabh/tfnightly/lib/python3.5/site-packages/tensorflow/python/layers/base.py", line 575, in __call__
outputs = self.call(inputs, *args, **kwargs)
File "/home/saurabh/tfnightly/lib/python3.5/site-packages/tensorflow/python/ops/rnn_cell_impl.py", line 438, in call
self._linear = _Linear([inputs, h], 4 * self._num_units, True)
File "/home/saurabh/tfnightly/lib/python3.5/site-packages/tensorflow/python/ops/rnn_cell_impl.py", line 1154, in __init__
shapes = [a.get_shape() for a in args]
File "/home/saurabh/tfnightly/lib/python3.5/site-packages/tensorflow/python/ops/rnn_cell_impl.py", line 1154, in <listcomp>
shapes = [a.get_shape() for a in args]
AttributeError: 'LSTMStateTuple' object has no attribute 'get_shape'
i convert my own grayscale dataset with 60*60 pixel to tfrecords with write_tfrecord() but when i want to read and decode them it causes error . what is the problem ?
train_tfrecord_addr = './data/train.tfrecords'
test_tfrecord_addr = './data/test.tfrecords'
n_train_samples = 43990
n_test_samples = 12500
batch_size = 32 # number of batches in each iteration
keep_prob = 0.5 # Dropout, probability to keep units
n_epochs = 25
tfrecords_filename = './data/test.tfrecords'
def _bytes_feature(value):
return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))
def _int64_feature(value):
return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))
def _float32_feature(value):
return tf.train.Feature(float_list=tf.train.FloatList(value=value))
def write_tfrecord(path):
images_addrs, images_labels = get_lable_and_image(path=path)
filename_pairs = list(zip(images_addrs, images_labels))
print(filename_pairs)
# to shuffle data
shuffle(filename_pairs)
writer = tf.python_io.TFRecordWriter(tfrecords_filename)
for img_path, label in filename_pairs:
# in this case all images are png with (32, 32) shape
img = np.array(Image.open(img_path)) # (32, 32) uint8
img_raw = img.tostring()
label_raw = label.tostring()
example = tf.train.Example(features=tf.train.Features(feature={
'image_raw': _bytes_feature(img_raw),
'label_raw': _bytes_feature(label_raw),
}))
writer.write(example.SerializeToString())
writer.close()
decode method ...
def read_and_decode(filename, batch_size, num_epochs, num_samples):
filename_queue = tf.train.string_input_producer([train_tfrecord_addr],
num_epochs=num_epochs)
reader = tf.TFRecordReader()
_, serialized_example = reader.read(filename_queue)
features = tf.parse_single_example(
serialized_example,
# Defaults are not specified since both keys are required.
features={
'image_raw': tf.FixedLenFeature([], tf.string),
'label_raw': tf.FixedLenFeature([], tf.string),
})
# Convert from a scalar string tensor to a uint8 tensor
image_raw = tf.decode_raw(features['image_raw'], tf.uint8)
image_resized = tf.reshape(image_raw, [60 * 60])
# Convert from [0, 255] -> [-0.5, 0.5] floats.
image_resized = tf.cast(image_resized, tf.float32) * (1. / 255) - 0.5
# Convert from a scalar string tensor to a uint8 tensor
label_raw = tf.decode_raw(features['label_raw'], tf.uint8)
label_resized = tf.reshape(label_raw, [2])
images, labels = tf.train.batch([image_resized, label_resized],
batch_size=batch_size,
capacity=num_samples,
num_threads=2, )
return images, labels
this is the main code that feed convolutions data .
def run_training():
"""Train ShapeNet for a number of steps."""
# Tell TensorFlow that the model will be built into the default Graph.
with tf.Graph().as_default():
# Input train images and labels.
train_images, train_labels = read_and_decode(filename=train_tfrecord_addr,
batch_size=batch_size,
num_epochs=n_epochs,
num_samples=n_train_samples)
# Input test images and labels.
# define batch_size = all test samples
test_images, test_labels = read_and_decode(filename=test_tfrecord_addr,
batch_size=n_test_samples,
num_epochs=n_epochs,
num_samples=n_test_samples)
# define placeholder for input images and labels
X = tf.placeholder(tf.float32, [None, 60 * 60])
Y = tf.placeholder(tf.float32, [None, 2])
# Build a Graph that computes predictions from the inference model.
prediction = convolutional_network_model(X)
# Backpropagation
# measure of error of our model
# this needs to be minimised by adjusting W and b
cross_entropy = -tf.reduce_sum(Y * tf.log(prediction))
# define training step which minimises cross entropy
train_op =tf.train.GradientDescentOptimizer(
learning_rate=0.001)
.minimize(cross_entropy)
# argmax gives index of highest entry in vector (1st axis of 1D tensor)
correct = tf.equal(tf.argmax(prediction, 1), tf.argmax(Y, 1))
# get mean of all entries in correct prediction, the higher the better
accuracy = tf.reduce_mean(tf.cast(correct, tf.float32))
# The op for initializing the variables.
init_op = tf.group(tf.global_variables_initializer(),
tf.local_variables_initializer())
st = time.time()
# Create a session for running operations in the Graph.
with tf.Session() as sess:
# Initialize the variables (the trained variables and the
# epoch counter).
sess.run(init_op)
# Start input enqueue threads.
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
for epoch in range(n_epochs):
for itr in range(n_train_samples // batch_size):
# fetch the batch train images and labels
batch_x, batch_y = sess.run([train_images, train_labels])
sess.run([train_op], feed_dict={X: batch_x, Y: batch_y})
# fetch whole test images and labels
batch_x, batch_y = sess.run([test_images, test_labels])
# feed the model with all test images and labels
acc, _ = sess.run([accuracy, train_op],
feed_dict={X: batch_x, Y: batch_y})
print('epoch %d/%d: , accuracy = %.3f'
% (epoch, n_epochs, acc))
# When done, ask the threads to stop.
coord.request_stop()
# Wait for threads to finish.
coord.join(threads)
et = time.time()
duration = et - st
print(duration)
this is the error . i also convert my dataset in binary format but again i see the same error
2018-02-13 13:53:02.401813: I C:\tf_jenkins\workspace\rel-
win\M\windows\PY\35\tensorflow\core\platform\cpu_feature_guard.cc:137] Your
CPU supports instructions that this TensorFlow binary was not compiled to
use: AVX
Traceback (most recent call last):
File "C:\Program Files\Python35\lib\site-
packages\tensorflow\python\client\session.py", line 1350, in _do_call
return fn(*args) File "C:\Program Files\Python35\lib\site-
packages\tensorflow\python\client\session.py", line 1329, in _run_fn
status, run_metadata)
File "C:\Program Files\Python35\lib\site-
packages\tensorflow\python\framework\errors_impl.py", line 473, in __exit__
c_api.TF_GetCode(self.status.status))
tensorflow.python.framework.errors_impl.OutOfRangeError: FIFOQueue
'_1_batch/fifo_queue' is closed and has insufficient elements (requested 32,
current size 0)
[[Node: batch = QueueDequeueManyV2[component_types=[DT_FLOAT, DT_UINT8],
timeout_ms=-1, _device="/job:localhost/replica:0/task:0/device:CPU:0"]
(batch/fifo_queue, batch/n)]]
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "F:/project/python/MathNet/mathnet.py", line 232, in <module>
run_training()
File "F:/project/python/MathNet/mathnet.py", line 207, in run_training
batch_x, batch_y = sess.run([train_images, train_labels])
File "C:\Program Files\Python35\lib\site-
packages\tensorflow\python\client\session.py", line 895, in run
run_metadata_ptr)
File "C:\Program Files\Python35\lib\site-
packages\tensorflow\python\client\session.py", line 1128, in _run
feed_dict_tensor, options, run_metadata)
File "C:\Program Files\Python35\lib\site-
packages\tensorflow\python\client\session.py", line 1344, in _do_run
options, run_metadata)
File "C:\Program Files\Python35\lib\site-
packages\tensorflow\python\client\session.py", line 1363, in _do_call
raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.OutOfRangeError: FIFOQueue
'_1_batch/fifo_queue' is closed and has insufficient elements (requested
32, current size 0)
[[Node: batch = QueueDequeueManyV2[component_types=[DT_FLOAT,
DT_UINT8], timeout_ms=-1,
_device="/job:localhost/replica:0/task:0/device:CPU:0"](batch/fifo_queue,
batch/n)]]
Caused by op 'batch', defined at:
File "F:/project/python/MathNet/mathnet.py", line 232, in <module>
run_training()
File "F:/project/python/MathNet/mathnet.py", line 159, in run_training
num_samples=n_train_samples)
File "F:/project/python/MathNet/mathnet.py", line 105, in
read_and_decode
num_threads=2, )
File "C:\Program Files\Python35\lib\site-
packages\tensorflow\python\training\input.py", line 979, in batch
name=name)
File "C:\Program Files\Python35\lib\site-
packages\tensorflow\python\training\input.py", line 754, in _batch
dequeued = queue.dequeue_many(batch_size, name=name)
File "C:\Program Files\Python35\lib\site-
packages\tensorflow\python\ops\data_flow_ops.py", line 475, in dequeue_many
self._queue_ref, n=n, component_types=self._dtypes, name=name)
File "C:\Program Files\Python35\lib\site-
packages\tensorflow\python\ops\gen_data_flow_ops.py", line 2764, in
_queue_dequeue_many_v2
component_types=component_types, timeout_ms=timeout_ms, name=name)
File "C:\Program Files\Python35\lib\site-
packages\tensorflow\python\framework\op_def_library.py", line 787, in
_apply_op_helper
op_def=op_def)
File "C:\Program Files\Python35\lib\site-
packages\tensorflow\python\framework\ops.py", line 3160, in create_op
op_def=op_def)
File "C:\Program Files\Python35\lib\site-
packages\tensorflow\python\framework\ops.py", line 1625, in __init__
self._traceback = self._graph._extract_stack() # pylint: disable=protected-
access
OutOfRangeError (see above for traceback): FIFOQueue '_1_batch/fifo_queue'
is closed and has insufficient elements (requested 32, current size 0)
[[Node: batch = QueueDequeueManyV2[component_types=[DT_FLOAT, DT_UINT8],
timeout_ms=-1, _device="/job:localhost/replica:0/task:0/device:CPU:0"]
(batch/fifo_queue, batch/n)]]
I construct a LSTM network, and my input's dimension is 100*100*83 ( batch_size=100, steps = 100, char_vector = 83). I build a two LSTM layers which has 512 hidden units.
# coding: utf-8
from __future__ import print_function
import tensorflow as tf
import numpy as np
import time
class CharRNN:
def __init__(self, num_classes, batch_size=64, num_steps=50, lstm_size=128, num_layers =2,\
learning_rate = 0.001, grad_clip=5, keep_prob=0.001,sampling= False):
# True for SGD
if sampling == True:
self.batch_size, self.num_steps = 1,1
else:
self.batch_size, self.num_steps = batch_size, num_steps
tf.reset_default_graph()
self.inputs, self.targets, self.keep_prob = self.build_inputs(self.batch_size,self.num_steps)
self.keep_prob = keep_prob
self.cell, self.initial_state = self.build_lstm(lstm_size,num_layers,self.batch_size,self.keep_prob)
# print(self.cell.state_size)
x_one_hot = tf.one_hot(self.inputs, num_classes)
print("cell state size: ",self.cell.state_size)
print("cell initial state: ",self.initial_state)
print("this is inputs", self.inputs)
print("x_one_hot: ",x_one_hot)
outputs, state = tf.nn.dynamic_rnn(self.cell, x_one_hot, initial_state= self.initial_state)
def build_inputs(self, num_seqs, num_steps):
inputs = tf.placeholder(tf.int32, shape=(num_seqs, num_steps), name = "inputs")
targets = tf.placeholder(tf.int32, shape= (num_seqs, num_steps), name="targets")
print('inputs shape: ',inputs.shape)
keep_prob = tf.placeholder(tf.float32, name="keep_prob")
return inputs, targets, keep_prob
def build_lstm(self, lstm_size, num_layers, batch_size, keep_prob):
# construct lstm cell
lstm = tf.nn.rnn_cell.BasicLSTMCell(lstm_size)
# add dropout
drop = tf.nn.rnn_cell.DropoutWrapper(lstm, output_keep_prob= keep_prob)
# stack multiple rnn cells
cell = tf.nn.rnn_cell.MultiRNNCell([drop for _ in range(num_layers)])
initial_state = cell.zero_state(batch_size, tf.float32)
return cell, initial_state
if __name__ == '__main__':
len_vocab = 83
batch_size = 100
num_steps = 100
lstm_size = 512
num_layers = 2
learning_rate = 0.001
keep_prob = 0.5
epochs = 20
save_every_n = 200
print("h1")
model = CharRNN(len_vocab, batch_size = batch_size, num_steps=num_steps, lstm_size = lstm_size,num_layers=num_layers\
,learning_rate=learning_rate,sampling= False,keep_prob = keep_prob
I get a dimension not match error at tf.nn.dynamic_rnn.
error message is like this:
inputs shape: (100, 100)
cell state size: (LSTMStateTuple(c=512, h=512), LSTMStateTuple(c=512, h=512))
cell initial state: (LSTMStateTuple(c=<tf.Tensor 'MultiRNNCellZeroState/DropoutWrapperZeroState/BasicLSTMCellZeroState/zeros:0' shape=(100, 512) dtype=float32>, h=<tf.Tensor 'MultiRNNCellZeroState/DropoutWrapperZeroState/BasicLSTMCellZeroState/zeros_1:0' shape=(100, 512) dtype=float32>), LSTMStateTuple(c=<tf.Tensor 'MultiRNNCellZeroState/DropoutWrapperZeroState_1/BasicLSTMCellZeroState/zeros:0' shape=(100, 512) dtype=float32>, h=<tf.Tensor 'MultiRNNCellZeroState/DropoutWrapperZeroState_1/BasicLSTMCellZeroState/zeros_1:0' shape=(100, 512) dtype=float32>))
this is inputs Tensor("inputs:0", shape=(100, 100), dtype=int32)
x_one_hot: Tensor("one_hot:0", shape=(100, 100, 83), dtype=float32)
Traceback (most recent call last):
File "./seq2_minimal.py", line 70, in <module>
,learning_rate=learning_rate,sampling= False,keep_prob = keep_prob)
File "./seq2_minimal.py", line 32, in __init__
outputs, state = tf.nn.dynamic_rnn(self.cell, x_one_hot, initial_state= self.initial_state)
File "/usr/local/lib/python2.7/site-packages/tensorflow/python/ops/rnn.py", line 614, in dynamic_rnn
dtype=dtype)
File "/usr/local/lib/python2.7/site-packages/tensorflow/python/ops/rnn.py", line 777, in _dynamic_rnn_loop
swap_memory=swap_memory)
File "/usr/local/lib/python2.7/site-packages/tensorflow/python/ops/control_flow_ops.py", line 2816, in while_loop
result = loop_context.BuildLoop(cond, body, loop_vars, shape_invariants)
File "/usr/local/lib/python2.7/site-packages/tensorflow/python/ops/control_flow_ops.py", line 2640, in BuildLoop
pred, body, original_loop_vars, loop_vars, shape_invariants)
File "/usr/local/lib/python2.7/site-packages/tensorflow/python/ops/control_flow_ops.py", line 2590, in _BuildLoop
body_result = body(*packed_vars_for_body)
File "/usr/local/lib/python2.7/site-packages/tensorflow/python/ops/rnn.py", line 762, in _time_step
(output, new_state) = call_cell()
File "/usr/local/lib/python2.7/site-packages/tensorflow/python/ops/rnn.py", line 748, in <lambda>
call_cell = lambda: cell(input_t, state)
File "/usr/local/lib/python2.7/site-packages/tensorflow/python/ops/rnn_cell_impl.py", line 183, in __call__
return super(RNNCell, self).__call__(inputs, state)
File "/usr/local/lib/python2.7/site-packages/tensorflow/python/layers/base.py", line 575, in __call__
outputs = self.call(inputs, *args, **kwargs)
File "/usr/local/lib/python2.7/site-packages/tensorflow/python/ops/rnn_cell_impl.py", line 1066, in call
cur_inp, new_state = cell(cur_inp, cur_state)
File "/usr/local/lib/python2.7/site-packages/tensorflow/python/ops/rnn_cell_impl.py", line 891, in __call__
output, new_state = self._cell(inputs, state, scope)
File "/usr/local/lib/python2.7/site-packages/tensorflow/python/ops/rnn_cell_impl.py", line 183, in __call__
return super(RNNCell, self).__call__(inputs, state)
File "/usr/local/lib/python2.7/site-packages/tensorflow/python/layers/base.py", line 575, in __call__
outputs = self.call(inputs, *args, **kwargs)
File "/usr/local/lib/python2.7/site-packages/tensorflow/python/ops/rnn_cell_impl.py", line 441, in call
value=self._linear([inputs, h]), num_or_size_splits=4, axis=1)
File "/usr/local/lib/python2.7/site-packages/tensorflow/python/ops/rnn_cell_impl.py", line 1189, in __call__
res = math_ops.matmul(array_ops.concat(args, 1), self._weights)
File "/usr/local/lib/python2.7/site-packages/tensorflow/python/ops/math_ops.py", line 1891, in matmul
a, b, transpose_a=transpose_a, transpose_b=transpose_b, name=name)
File "/usr/local/lib/python2.7/site-packages/tensorflow/python/ops/gen_math_ops.py", line 2437, in _mat_mul
name=name)
File "/usr/local/lib/python2.7/site-packages/tensorflow/python/framework/op_def_library.py", line 787, in _apply_op_helper
op_def=op_def)
File "/usr/local/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 2958, in create_op
set_shapes_for_outputs(ret)
File "/usr/local/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 2209, in set_shapes_for_outputs
shapes = shape_func(op)
File "/usr/local/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 2159, in call_with_requiring
return call_cpp_shape_fn(op, require_shape_fn=True)
File "/usr/local/lib/python2.7/site-packages/tensorflow/python/framework/common_shapes.py", line 627, in call_cpp_shape_fn
require_shape_fn)
File "/usr/local/lib/python2.7/site-packages/tensorflow/python/framework/common_shapes.py", line 691, in _call_cpp_shape_fn_impl
raise ValueError(err.message)
ValueError: Dimensions must be equal, but are 1024 and 595 for 'rnn/while/rnn/multi_rnn_cell/cell_0/cell_0/basic_lstm_cell/MatMul_1' (op: 'MatMul') with input shapes: [100,1024], [595,2048].
I search that and find that tensorflow's lstm cell should adjust its input size automatically. But error message said this.
It shows
input size is [100, 1024] and lstm is [595, 2048].
Thanks firstly.
cell = tf.nn.rnn_cell.MultiRNNCell([drop for _ in range(num_layers)])
TO
cell = tf.nn.rnn_cell.MultiRNNCell([drop])
because your given input tensor and produces tensor are not the same.
I am trying to learn Tensor Flow and so I followed this tutorial on Neural Networks by https://pythonprogramming.net/tensorflow-neural-network-session-machine-learning-tutorial/
I am trying to run the code, but keep getting the same dimension Error even when my dimensions seem correct.
I am new to Tensor Flow, so I am not exactly sure what am I doing wrong.
I'll post the code and the error.
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)
n_nodes_hl1 = 500
n_nodes_hl2 = 500
n_nodes_hl3 = 500
n_classes = 10
batch_size = 100
x = tf.placeholder('float', [None,784])
y = tf.placeholder('float')
def neural_network_model(data):
#(input_data * weights) + biases
hidden_1_layer = {
'weights' : tf.Variable(tf.random_normal([784,n_nodes_hl1])),
'biases' : tf.Variable(tf.random_normal([n_nodes_hl1]))
}
hidden_2_layer = {
'weights' : tf.Variable(tf.random_normal([n_nodes_hl1,n_nodes_hl2])),
'biases' : tf.Variable(tf.random_normal([n_nodes_hl2]))
}
hidden_3_layer = {
'weights' : tf.Variable(tf.random_normal([n_nodes_hl2,n_nodes_hl3])),
'biases' : tf.Variable(tf.random_normal([n_nodes_hl3]))
}
output_layer = {
'weights' : tf.Variable(tf.random_normal([n_nodes_hl3,n_classes])),
'biases' : tf.Variable(tf.random_normal([n_classes]))
}
net_Layer1 = tf.add(tf.multiply(data, hidden_1_layer['weights']), hidden_1_layer['biases'])
output_layer1 = tf.nn.relu(net_Layer1)
net_Layer2 = tf.add(tf.multiply(output_layer1, hidden_2_layer['weights']), hidden_2_layer['biases'])
output_layer2 = tf.nn.relu(net_Layer2)
net_Layer3 = tf.add(tf.multiply(output_layer2, hidden_3_layer['weights']), hidden_3_layer['biases'])
output_layer3 = tf.nn.relu(net_Layer3)
output = tf.add(tf.multiply(output_layer3, output_layer['weights']), output_layer['biases'])
return output
def train_neural_network(input):
prediction = neural_network_model(input)
error = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits = prediction,labels = y))
optimizer = tf.train.AdamOptimizer().minimize(error)
epochs = 10
with tf.Session() as sess:
sess.run(tf.global_variables_initializer)
for epoch in epochs:
epoch_loss = 0
for _ in range(int(mnist.train.num_examples/batch_size)):
epoch_x, epoch_y = mnist.train.next_batch(batch_size)
_, e = sess.run([optimizer, error], feed_dict={x:epoch_x, y:epoch_y})
epoch_loss += e
print('Epoch', epoch, 'completed out of', epochs, 'loss :', epoch_loss)
correct = tf.equal(tf.argmax(prediction, 1), tf.argmax(y,1))
accuracy = tf.reduce_mean(tf.cast(correct, 'float'))
print('Accuracy:', accuracy.eval({x.mnist.test.images, y.mnist.test.labels}))
train_neural_network(x)
The error I am getting is the following-
net_Layer1 = tf.add(tf.multiply(data, hidden_1_layer['weights']), hidden_1_layer['biases'])
File "/usr/local/lib/python2.7/site-packages/tensorflow/python/ops/math_ops.py", line 357, in multiply
return gen_math_ops._mul(x, y, name)
File "/usr/local/lib/python2.7/site-packages/tensorflow/python/ops/gen_math_ops.py", line 1625, in _mul
result = _op_def_lib.apply_op("Mul", x=x, y=y, name=name)
File "/usr/local/lib/python2.7/site-packages/tensorflow/python/framework/op_def_library.py", line 763, in apply_op
op_def=op_def)
File "/usr/local/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 2397, in create_op
set_shapes_for_outputs(ret)
File "/usr/local/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 1757, in set_shapes_for_outputs
shapes = shape_func(op)
File "/usr/local/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 1707, in call_with_requiring
return call_cpp_shape_fn(op, require_shape_fn=True)
File "/usr/local/lib/python2.7/site-packages/tensorflow/python/framework/common_shapes.py", line 610, in call_cpp_shape_fn
debug_python_shape_fn, require_shape_fn)
File "/usr/local/lib/python2.7/site-packages/tensorflow/python/framework/common_shapes.py", line 675, in _call_cpp_shape_fn_impl
raise ValueError(err.message)
ValueError: Dimensions must be equal, but are 784 and 500 for 'Mul' (op: 'Mul') with input shapes: [?,784], [784,500].
The error comes because you use "multiply"
In all the lines where you use
tf.add(tf.multiply(.....))
Use:
tf.add(tf.matmul(......))
Because this is the matrix multiplication.