Tensorflow:使用我自己的数据的MNIST卷积网络

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

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()

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].

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