I'm trying to to use the Convolutional Network tutorial https://www.tensorflow.org/tutorials/layers as base with my own dataset, I was currently trying to use this dataset https://ai.stanford.edu/~jkrause/cars/car_dataset.html. I created this reader.py file to serve the data and looks like its working fine
import tensorflow as tf
from os import listdir
from os.path import isfile, join, dirname, realpath
def my_input_fn():
this_path = dirname(realpath(__file__))
data_path = this_path + "/cars_train/"
f = open(this_path + "/train_perfect_preds.txt")
labels = [int(line.rstrip()) for line in f]
f.close()
files = ["/cars_train/" + f for f in listdir(data_path)]
files.sort()
def _parse_function(filename, label):
image_string = tf.read_file(filename)
image_decoded = tf.image.decode_jpeg(image_string,3)
image_resized = tf.image.resize_images(image_decoded, [28, 28])
return image_resized, label
filenames = tf.constant(files)
labels = tf.constant(labels)
dataset = tf.data.Dataset.from_tensor_slices((filenames, labels))
dataset = dataset.map(_parse_function)
iterator = dataset.make_one_shot_iterator()
imagen, label = iterator.get_next()
return imagen, label
I tried to call it from this file https://github.com/tensorflow/tensorflow/blob/r1.7/tensorflow/examples/tutorials/layers/cnn_mnist.py
Changing the Main() function and the dropout layer to adjust to my number of classes
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import tensorflow as tf
import reader as cars
tf.logging.set_verbosity(tf.logging.INFO)
def cnn_model_fn(features, labels, mode):
input_layer = tf.reshape(features, [-1, 28, 28, 3])
conv1 = tf.layers.conv2d(
inputs=input_layer,
filters=32,
kernel_size=[5, 5],
padding="same",
activation=tf.nn.relu)
pool1 = tf.layers.max_pooling2d(inputs=conv1, pool_size=[2, 2], strides=2)
conv2 = tf.layers.conv2d(
inputs=pool1,
filters=64,
kernel_size=[5, 5],
padding="same",
activation=tf.nn.relu)
pool2 = tf.layers.max_pooling2d(inputs=conv2, pool_size=[2, 2], strides=2)
pool2_flat = tf.reshape(pool2, [-1, 7 * 7 * 64])
dense = tf.layers.dense(inputs=pool2_flat, units=1024, activation=tf.nn.relu)
dropout = tf.layers.dropout(
inputs=dense, rate=0.4, training=mode == tf.estimator.ModeKeys.TRAIN)
logits = tf.layers.dense(inputs=dropout, units=196)
predictions = {
# Generate predictions (for PREDICT and EVAL mode)
"classes": tf.argmax(input=logits, axis=1),
# Add `softmax_tensor` to the graph. It is used for PREDICT and by the
# `logging_hook`.
"probabilities": tf.nn.softmax(logits, name="softmax_tensor")
}
if mode == tf.estimator.ModeKeys.PREDICT:
return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions)
# Calculate Loss (for both TRAIN and EVAL modes)
loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits)
# Configure the Training Op (for TRAIN mode)
if mode == tf.estimator.ModeKeys.TRAIN:
optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.001)
train_op = optimizer.minimize(
loss=loss,
global_step=tf.train.get_global_step())
return tf.estimator.EstimatorSpec(mode=mode, loss=loss, train_op=train_op)
# Add evaluation metrics (for EVAL mode)
eval_metric_ops = {
"accuracy": tf.metrics.accuracy(
labels=labels, predictions=predictions["classes"])}
return tf.estimator.EstimatorSpec(
mode=mode, loss=loss, eval_metric_ops=eval_metric_ops)
def main(unused_argv):
# Create the Estimator
classifier = tf.estimator.Estimator(
model_fn=cnn_model_fn, model_dir="/tmp/mnist_convnet_model")
# Set up logging for predictions
# Log the values in the "Softmax" tensor with label "probabilities"
tensors_to_log = {"probabilities": "softmax_tensor"}
logging_hook = tf.train.LoggingTensorHook(
tensors=tensors_to_log, every_n_iter=50)
# Train the model
classifier.train(
input_fn= lambda: cars.my_input_fn(),
steps=20000,
hooks=[logging_hook])
eval_results = classifier.evaluate(input_fn =lambda: my_input_fn())
print(eval_results)
if __name__ == "__main__":
tf.app.run()
But I get an error
INFO:tensorflow:Using default config.
INFO:tensorflow:Using config: {'_save_checkpoints_secs': 600, '_session_config': None, '_keep_checkpoint_max': 5, '_task_type': 'worker', '_global_id_in_cluster': 0, '_is_chief': True, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x7fd4a5264790>, '_evaluation_master': '', '_save_checkpoints_steps': None, '_keep_checkpoint_every_n_hours': 10000, '_service': None, '_num_ps_replicas': 0, '_tf_random_seed': None, '_master': '', '_num_worker_replicas': 1, '_task_id': 0, '_log_step_count_steps': 100, '_model_dir': '/tmp/mnist_convnet_model', '_save_summary_steps': 100}
INFO:tensorflow:Calling model_fn.
Traceback (most recent call last):
File "test.py", line 116, in <module>
tf.app.run()
File "/home/satler/TFG/testCifar10/tensor/local/lib/python2.7/site-packages/tensorflow/python/platform/app.py", line 126, in run
_sys.exit(main(argv))
File "test.py", line 109, in main
hooks=[logging_hook])
File "/home/satler/TFG/testCifar10/tensor/local/lib/python2.7/site-packages/tensorflow/python/estimator/estimator.py", line 352, in train
loss = self._train_model(input_fn, hooks, saving_listeners)
File "/home/satler/TFG/testCifar10/tensor/local/lib/python2.7/site-packages/tensorflow/python/estimator/estimator.py", line 812, in _train_model
features, labels, model_fn_lib.ModeKeys.TRAIN, self.config)
File "/home/satler/TFG/testCifar10/tensor/local/lib/python2.7/site-packages/tensorflow/python/estimator/estimator.py", line 793, in _call_model_fn
model_fn_results = self._model_fn(features=features, **kwargs)
File "test.py", line 73, in cnn_model_fn
loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits)
File "/home/satler/TFG/testCifar10/tensor/local/lib/python2.7/site-packages/tensorflow/python/ops/losses/losses_impl.py", line 831, in sparse_softmax_cross_entropy
labels, logits, weights, expected_rank_diff=1)
File "/home/satler/TFG/testCifar10/tensor/local/lib/python2.7/site-packages/tensorflow/python/ops/losses/losses_impl.py", line 760, in _remove_squeezable_dimensions
labels, predictions, expected_rank_diff=expected_rank_diff)
File "/home/satler/TFG/testCifar10/tensor/local/lib/python2.7/site-packages/tensorflow/python/ops/confusion_matrix.py", line 75, in remove_squeezable_dimensions
predictions = array_ops.squeeze(predictions, [-1])
File "/home/satler/TFG/testCifar10/tensor/local/lib/python2.7/site-packages/tensorflow/python/ops/array_ops.py", line 2568, in squeeze
return gen_array_ops._squeeze(input, axis, name)
File "/home/satler/TFG/testCifar10/tensor/local/lib/python2.7/site-packages/tensorflow/python/ops/gen_array_ops.py", line 5169, in _squeeze
"Squeeze", input=input, squeeze_dims=axis, name=name)
File "/home/satler/TFG/testCifar10/tensor/local/lib/python2.7/site-packages/tensorflow/python/framework/op_def_library.py", line 787, in _apply_op_helper
op_def=op_def)
File "/home/satler/TFG/testCifar10/tensor/local/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 3273, in create_op
compute_device=compute_device)
File "/home/satler/TFG/testCifar10/tensor/local/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 3313, in _create_op_helper
set_shapes_for_outputs(op)
File "/home/satler/TFG/testCifar10/tensor/local/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 2501, in set_shapes_for_outputs
return _set_shapes_for_outputs(op)
File "/home/satler/TFG/testCifar10/tensor/local/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 2474, in _set_shapes_for_outputs
shapes = shape_func(op)
File "/home/satler/TFG/testCifar10/tensor/local/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 2404, in call_with_requiring
return call_cpp_shape_fn(op, require_shape_fn=True)
File "/home/satler/TFG/testCifar10/tensor/local/lib/python2.7/site-packages/tensorflow/python/framework/common_shapes.py", line 627, in call_cpp_shape_fn
require_shape_fn)
File "/home/satler/TFG/testCifar10/tensor/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: Can not squeeze dim[1], expected a dimension of 1, got 196 for 'sparse_softmax_cross_entropy_loss/remove_squeezable_dimensions/Squeeze' (op: 'Squeeze') with input shapes: [1,196].
I'm not trying to get decent result just trying make it work so I can start building my own network on top of something that works. I'm not sure if my problem comes from how I'm loading the dataset or the network architecture any hint would be very much apprenticed. This is my first question sorry if the if the information is insufficient or anything else.
Where cars_train contains the cars jpg with a numerical name (00001.jpg ...) and train_perfect_preds.txt contains one line per imagen with a number as class. First Imagen correspond to first line of the .txt and so on.
