Here is the example I am testing on MNIST dataset for quantization. I am testing my model using below code:
import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data from tensorflow.python.framework import graph_util from tensorflow.core.framework import graph_pb2 import numpy as np def test_model(model_file,x_in): with tf.Session() as sess: with open(model_file, "rb") as f: output_graph_def = graph_pb2.GraphDef() output_graph_def.ParseFromString(f.read()) _ = tf.import_graph_def(output_graph_def, name="") x = sess.graph.get_tensor_by_name('Placeholder_1:0') y = sess.graph.get_tensor_by_name('softmax_cross_entropy_with_logits:0') new_scores = sess.run(y, feed_dict={x:x_in.test.images}) print((orig_scores - new_scores) < 1e-6) find_top_pred(orig_scores) find_top_pred(new_scores) #print(epoch_x.shape) mnist = input_data.read_data_sets("/tmp/data/", one_hot = True) test_model('mnist_cnn1.pb',mnist) I am not getting where I am providing incorrect values. Here I have added the complete track of error code. Below is the error:
Extracting /tmp/data/train-images-idx3-ubyte.gz Extracting /tmp/data/train-labels-idx1-ubyte.gz Extracting /tmp/data/t10k-images-idx3-ubyte.gz Extracting /tmp/data/t10k-labels-idx1-ubyte.gz Traceback (most recent call last): File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py", line 1323, in _do_call return fn(*args) File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py", line 1302, in _run_fn status, run_metadata) File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/errors_impl.py", line 473, in __exit__ c_api.TF_GetCode(self.status.status)) tensorflow.python.framework.errors_impl.InvalidArgumentError: You must feed a value for placeholder tensor 'Placeholder' with dtype float and shape [?,784] [[Node: Placeholder = Placeholder[dtype=DT_FLOAT, shape=[?,784], _device="/job:localhost/replica:0/task:0/device:CPU:0"]()]] During handling of the above exception, another exception occurred:
Traceback (most recent call last): File "tmp.py", line 26, in <module> test_model('/home/shringa/tensorflowdata/mnist_cnn1.pb',mnist) File "tmp.py", line 19, in test_model new_scores = sess.run(y, feed_dict={x:x_in.test.images}) File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py", line 889, in run run_metadata_ptr) File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py", line 1120, in _run feed_dict_tensor, options, run_metadata) File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py", line 1317, in _do_run options, run_metadata) File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py", line 1336, in _do_call raise type(e)(node_def, op, message) tensorflow.python.framework.errors_impl.InvalidArgumentError: You must feed a value for placeholder tensor 'Placeholder' with dtype float and shape [?,784] [[Node: Placeholder = Placeholder[dtype=DT_FLOAT, shape=[?,784], _device="/job:localhost/replica:0/task:0/device:CPU:0"]()]] Caused by op 'Placeholder', defined at: File "tmp.py", line 26, in <module> test_model('/home/shringa/tensorflowdata/mnist_cnn1.pb',mnist) File "tmp.py", line 16, in test_model _ = tf.import_graph_def(output_graph_def, name="") File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/util/deprecation.py", line 316, in new_func return func(*args, **kwargs) File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/importer.py", line 411, in import_graph_def op_def=op_def) File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py", line 3069, in create_op op_def=op_def) File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py", line 1579, in __init__ self._traceback = self._graph._extract_stack() # pylint: disable=protected-access InvalidArgumentError (see above for traceback): You must feed a value for placeholder tensor 'Placeholder' with dtype float and shape [?,784] [[Node: Placeholder = Placeholder[dtype=DT_FLOAT, shape=[?,784], _device="/job:localhost/replica:0/task:0/device:CPU:0"]()]] As shown above I am using mnist_cnn1.pb file to extract my model and to test it on mnist test images but it is throwing error of shape of the placeholder.
Below shown is my cnn model:
import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets("/tmp/data/", one_hot = True) print(type(mnist)); n_classes = 10 batch_size = 128 x = tf.placeholder(tf.float32, [None, 784]) y = tf.placeholder(tf.float32) def conv2d(x, W): return tf.nn.conv2d(x, W, strides=[1,1,1,1], padding= 'SAME') def maxpool2d(x): # size of window movement of window return tf.nn.max_pool(x, ksize =[1,2,2,1], strides= [1,2,2,1], padding = 'SAME') def convolutional_network_model(x): weights = {'W_conv1':tf.Variable(tf.random_normal([5,5,1,32])), 'W_conv2':tf.Variable(tf.random_normal([5,5,32,64])), 'W_fc':tf.Variable(tf.random_normal([7*7*64,1024])), 'out':tf.Variable(tf.random_normal([1024, n_classes]))} biases = {'B_conv1':tf.Variable(tf.random_normal([32])), 'B_conv2':tf.Variable(tf.random_normal([64])), 'B_fc':tf.Variable(tf.random_normal([1024])), 'out':tf.Variable(tf.random_normal([n_classes]))} x = tf.reshape(x, shape=[-1,28,28,1]) conv1 = conv2d(x, weights['W_conv1']) conv1 = maxpool2d(conv1) conv2 = conv2d(conv1, weights['W_conv2']) conv2 = maxpool2d(conv2) fc =tf.reshape(conv2,[-1,7*7*64]) fc = tf.nn.relu(tf.matmul(fc, weights['W_fc'])+ biases['B_fc']) output = tf.matmul(fc, weights['out']+biases['out']) return output def train_neural_network(x): prediction = convolutional_network_model(x) # OLD VERSION: #cost = tf.reduce_mean( tf.nn.softmax_cross_entropy_with_logits(prediction,y) ) # NEW: cost = tf.reduce_mean( tf.nn.softmax_cross_entropy_with_logits_v2(logits=prediction, labels=y) ) optimizer = tf.train.AdamOptimizer().minimize(cost) hm_epochs = 25 with tf.Session() as sess: # OLD: #sess.run(tf.initialize_all_variables()) # NEW: sess.run(tf.global_variables_initializer()) 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) _, 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, tf.float32)) print('Accuracy:',accuracy.eval({x:mnist.test.images, y:mnist.test.labels})) train_neural_network(x) and by using bazel I have created mnist_cnn1.pb file:
python3 tensorflow/tools/quantization/quantize_graph.py --input=/home/shringa/tensorflowdata/mnist_cnn.pb --output=/home/shringa/tensorflowdata/mnist_cnn1.pb --output_node_names=softmax_cross_entropy_with_logits --mode=eightbit bazel-bin/tensorflow/tools/graph_transforms/summarize_graph --in_graph=/home/shringa/tensorflowdata/mnist_cnn1.pb