Develop Reference

How to resolve ValueError: Cannot feed value of shape (230,) for Tensor 'Placeholder_1:0', which has shape '(?, 4) in python using Jupyter notebook

I can't seem to resolve the issue as I am new to tensorflow I think the issue is the graph mismatching
but cant resolve it please help. I want to resolve this as i will be using this for android app.
Defining placeholders:
x = tf.placeholder(tf.float32,shape=[None,80,80,3])
y_true = tf.placeholder(tf.float32,shape=[None,4])
CNN Layer:
%%time
#Changing settings for GPU running.
config = tf.ConfigProto(allow_soft_placement=True)
config.gpu_options.allow_growth = True
config.gpu_options.allocator_type = 'BFC'
#Training and saving the result
with tf.Session(config=config) as sess:
sess.run(init)
summary_writer = tf.summary.FileWriter(TRAIN_DIR, graph=tf.get_default_graph())
for i in range(epochs):
for j in range(0,steps,step_size):
_ , c , summary,d = sess.run([train,cross_entropy,merged_summary_op,acc],feed_dict={x:X[j:j+step_size] , y_true:Y[j:j+step_size] ,hold_prob1:0.5,hold_prob2:0.5,hold_prob3:0.5,hold_prob4:0.5})
summary_writer.add_summary(summary, i * total_batch + j)
acc_train.append(d)
mean_of_cross_entropy = sess.run(cross_entropy,feed_dict={x:cv_x,y_true:cv_y ,hold_prob1:1.0,hold_prob2:1.0,hold_prob3:1.0,hold_prob4:1.0})
mean_of_acc = sess.run(acc,feed_dict={x:cv_x ,y_true:cv_y ,hold_prob1:1.0,hold_prob2:1.0,hold_prob3:1.0,hold_prob4:1.0})
cross_entropy_list.append(mean_of_cross_entropy)
acc_list.append(mean_of_acc)
print(i,mean_of_cross_entropy,mean_of_acc)
saver.save(sess, "C:\\Users\\blessie\\Desktop\DATASETS - TOMATO LEAF DISEASE\\Models\\CNN_MC.ckpt")
print("test accuracy = ",np.mean([sess.run(acc,feed_dict={x:test_x[:230],y_true:test_y[:230] ,hold_prob1:1.0,hold_prob2:1.0,hold_prob3:1.0,hold_prob4:1.0}),sess.run(acc,feed_dict={x:test_x[230:460],y_true:test_y[230:460] ,hold_prob1:1.0,hold_prob2:1.0,hold_prob3:1.0,hold_prob4:1.0}),sess.run(acc,feed_dict={x:test_x[460:],y_true:test_y[460:] ,hold_prob1:1.0,hold_prob2:1.0,hold_prob3:1.0,hold_prob4:1.0})]))
print("cross_entropy loss = ",np.mean([sess.run(cross_entropy,feed_dict={x:test_x[:230],y_true:test_y[:230] ,hold_prob1:1.0,hold_prob2:1.0,hold_prob3:1.0,hold_prob4:1.0}),sess.run(cross_entropy,feed_dict={x:test_x[230:460],y_true:test_y[230:460] ,hold_prob1:1.0,hold_prob2:1.0,hold_prob3:1.0,hold_prob4:1.0}),sess.run(cross_entropy,feed_dict={x:test_x[460:],y_true:test_y[460:] ,hold_prob1:1.0,hold_prob2:1.0,hold_prob3:1.0,hold_prob4:1.0})]))
After running the code and pre-processing the data, I got a value error stating:
ValueError: Cannot feed value of shape (230,) for Tensor 'Placeholder_1:0', which has shape '(?, 4)'
How can I resolve this problem/error?

You haven't given the full code, but from the error message, it appears that your y_true placeholder is being supplied with data of shape [230] which is basically of type [None]. So changing your y_true to should solve this particular problem:
y_true = tf.placeholder(tf.float32,shape=[None])
But you have used y_true previously in your code already. So, is the shape of cv_y correctly being fed as [None, 4].
Other particular thing what I can guess is you are supplying one hot encoded values to cv_y already but in the subsequent lines of code you are not. So, you can change your code as below:
y_true = tf.placeholder(tf.float32,shape=[None])
y_true = tf.one_hot(y_true, 4)
And now, you will have to change your cv_y just before you are passing it in feed_dict as
cv_y = np.argmax(cv_y, axis=-1)

Adding base64 decoding layer before input layer in Tensorflow Graph

I want to add a layer to decode a base64 string to a numpy array which is a JPEG image. This layer needs to be in front of my original first layer of the model. I want to add a decoding layer because of this way I can reduce the network traffic and the speed.
I consist of a *.pb file which i load into a graph by this I try to manipulate that graph with the graph_editor module of Tensorflow which i'm stuck at.
def decode(entry):
extracted_image = tf.io.decode_jpeg(tf.io.decode_base64(entry))
expanded = tf.expand_dims(extracted_image, axis=0)
resized = tf.image.resize_images(expanded, size=[320, 320])
squeezed = tf.squeeze(resized, axis=0)
return squeezed
with tf.Session() as sess:
print("load graph")
with tf.gfile.FastGFile(GRAPH_PB_PATH, 'rb') as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
sess.graph.as_default()
tf.import_graph_def(graph_def, name='')
graph_nodes = [n for n in graph_def.node]
# Get Graph
g = tf.get_default_graph()
input_feat = tf.placeholder(shape=(), dtype=tf.string, name="input_images_str")
decoded_input = tf.map_fn(decode, input_feat, dtype=tf.float32)
input_tensor = g.get_tensor_by_name("input_images:0")
_conn = graph_editor.connect(decoded_input, input_tensor)
print(_conn)
I also tried connecting the original input_tensor with the decoding layer in Tensorflow.
input_tensor = tf.get_default_graph().get_tensor_by_name("input_images:0")
input_feat = tf.placeholder(shape=(), dtype=tf.string, name="input_images_str")
decoded_input = tf.map_fn(decode, input_feat, dtype=tf.float32)
input_tensor = tf.matmul(decoded_input, input_tensor)
output_tensor = tf.get_default_graph().get_tensor_by_name("feature_fusion/concat_3:0")
# Saving
inputs = {
"input_images_str": input_feat
}
outputs = {"feature_fusion/concat_3": output_tensor}
tf.saved_model.simple_save(
sess, './tf_model/1', inputs, outputs
)
The last method gave this back when making a prediction with a base64 image.
InvalidArgumentError (see above for traceback): You must feed a value for placeholder tensor 'input_images' with dtype float and shape [?,?,?,3]

tensor flow input variable error

I am creating a tensor flow code and get an error when I try to run with variables.
The base code is
import tensor flow as tf
import numpy as np
graph = tf.Graph()
with graph.as_default():
with tf.name_scope("variables"):
# keep track of how many times the model has been run
global_step = tf.Variable(0, dtype=tf.int32, trainable=False, name="global_step")
# keep track of sum of all outputs over time
total_output = tf.Variable(0, dtype=tf.float32, trainable=False, name="total_output")
with tf.name_scope("transformation"):
# separate input layer
with tf.name_scope("input"):
# create input placeholder which takes in a vector
a = tf.placeholder(tf.float32, shape=[None], name = "input_placeholder_A")
#separate the middle layer
with tf.name_scope("middle"):
b = tf.reduce_prod(a, name = "product_b")
c = tf.reduce_sum(a, name = "sum_c")
# separate the output layer
with tf.name_scope("output"):
output = tf.add(b,c, name="output")
# separate the update layer and store the variables
with tf.name_scope("update"):
update_total = total_output.assign(output)
increment_step = global_step.assign_add(1)
# now create namescope summaries and store these in the summary
with tf.name_scope("summaries"):
avg = tf.divide(update_total, tf.cast(increment_step, tf.float32), name = "average")
# create summary for output node
tf.summary.scalar("output_summary", output)
tf.summary.scalar("total_summary",update_total)
tf.summary.scalar("average_summary",avg)
with tf.name_scope("global_ops"):
init = tf.initialize_all_variables()
merged_summaries = tf.summary.merge_all()
sess = tf.Session(graph=graph)
writer = tf.summary.FileWriter('./improved_graph', graph)
sess.run(init)
def run_graph(input_tensor):
feed_dict = {a: input_tensor}
_, step, summary = sess.run([output, increment_step, merged_summaries],feed_dict=feed_dict)
writer.add_summary(summary, global_step=step)
when I try to run the above code
run_graph([2,8])
I get the error
InvalidArgumentError Traceback (most recent call
last) InvalidArgumentError (see above for traceback): You must feed a
value for placeholder tensor
'transformation_2/input/input_placeholder_A' with dtype float and
shape [?][[Node: transformation_2/input/input_placeholder_A =
Placeholderdtype=DT_FLOAT, shape=[?],
_device="/job:localhost/replica:0/task:0/device:CPU:0"]]
I do not understand what I am doing wrong in this since the code is all corrected for the version of tensor flow installed.

Your placeholder a is defined as being of type float32 but [5, 8] contain int values.
run_graph([2., 8.]) or run_graph(np.array([5, 8], dtype=np.float32)) should work.

tensorflow shuffle_batch and feed_dict error

This is the main part of my code.
I'm confused on function shuffle_batch and feed_dict.
In my code below, the features and labels I put into the function are "list".(I also tried "array" before.But it seems doesn't matter.)
What I want to do is make my testing data(6144,26) and training data(1024,13) into batch:(100,26) and (100,13),then set them as the feed_dict for the placeholders.
My questions are:
1.The outputs of the function tf.train.batch_shuffle are Tensors.But I can not put tensors in the feed_dict,right?
2.When I compiled the last two rows,error says,got shape [6144, 26], but wanted [6144] .I know it may be a dimension error,but how can I fix it.
Thanks a lot.
import tensorflow as tf
import scipy.io as sio
#import signal matfile
#[('label', (8192, 13), 'double'), ('clipped_DMT', (8192, 26), 'double')]
file = sio.loadmat('DMTsignal.mat')
#get array(clipped_DMT)
data_cDMT = file['clipped_DMT']
#get array(label)
data_label = file['label']
with tf.variable_scope('split_cDMT'):
cDMT_test_list = []
cDMT_training_list = []
for i in range(0,8192):
if i % 4 == 0:
cDMT_test_list.append(data_cDMT[i])
else:
cDMT_training_list.append(data_cDMT[i])
with tf.variable_scope('split_label'):
label_test_list = []
label_training_list = []
for i in range(0,8192):
if i % 4 == 0:
label_test_list.append(data_label[i])
else:
label_training_list.append(data_label[i])
#set parameters
n_features = cDMT_training.shape[1]
n_labels = label_training.shape[1]
learning_rate = 0.8
hidden_1 = 256
hidden_2 = 128
training_steps = 1000
BATCH_SIZE = 100
#set Graph input
with tf.variable_scope('cDMT_Inputs'):
X = tf.placeholder(tf.float32,[None, n_features],name = 'Input_Data')
with tf.variable_scope('labels_Inputs'):
Y = tf.placeholder(tf.float32,[None, n_labels],name = 'Label_Data')
#set variables
#Initialize both W and b as tensors full of zeros
with tf.variable_scope('layerWeights'):
h1 = tf.Variable(tf.random_normal([n_features,hidden_1]))
h2 = tf.Variable(tf.random_normal([hidden_1,hidden_2]))
w_out = tf.Variable(tf.random_normal([hidden_2,n_labels]))
with tf.variable_scope('layerBias'):
b1 = tf.Variable(tf.random_normal([hidden_1]))
b2 = tf.Variable(tf.random_normal([hidden_2]))
b_out = tf.Variable(tf.random_normal([n_labels]))
#create model
def neural_net(x):
layer_1 = tf.add(tf.matmul(x,h1),b1)
layer_2 = tf.nn.relu(tf.add(tf.matmul(layer_1,h2),b2))
out_layer = tf.add(tf.matmul(layer_2,w_out),b_out)
return out_layer
nn_out = neural_net(X)
#loss and optimizer
with tf.variable_scope('Loss'):
loss = tf.reduce_mean(tf.reduce_sum(tf.nn.softmax_cross_entropy_with_logits(logits = nn_out,labels = Y)))
with tf.name_scope('Train'):
optimizer = tf.train.AdamOptimizer(learning_rate).minimize(loss)
with tf.name_scope('Accuracy'):
correct_prediction = tf.equal(tf.argmax(nn_out,1),tf.argmax(Y,1))
#correct_prediction = tf.metrics.accuracy (labels = Y, predictions =nn_out)
acc = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))
# Initialize
init = tf.global_variables_initializer()
# start computing & training
with tf.Session() as sess:
sess.run(init)
for step in range(training_steps):
#set batch
cmt_train_bat,label_train_bat = sess.run(tf.train.shuffle_batch([cDMT_training_list,label_training_list],batch_size = BATCH_SIZE,capacity=50000,min_after_dequeue=10000))
cmt_test_bat,label_test_bat = sess.run(tf.train.shuffle_batch([cDMT_test_list,label_test_list],batch_size = BATCH_SIZE,capacity=50000,min_after_dequeue=10000))

From the Session.run doc:
The optional feed_dict argument allows the caller to override the
value of tensors in the graph. Each key in feed_dict can be one of the
following types:
If the key is a tf.Tensor, the value may be a Python scalar, string,
list, or numpy ndarray that can be converted to the same dtype as that
tensor. Additionally, if the key is a tf.placeholder, the shape of the
value will be checked for compatibility with the placeholder.
...
So you are right: for X and Y (which are placeholders) you can't feed a tensor and tf.train.shuffle_batch is not designed to work with placeholders.
You can follow one of two ways:
get rid of placeholders and use tf.TFRecordReader in combination with tf.train.shuffle_batch, as suggested here. This way you'll have only tensors in your model and you won't need to "feed" anything additionally.
batch and shuffle the data yourself in numpy and feed into placeholders. This takes just several lines of code, so I find it easier, though both paths are valid.
Take also into account performance considerations.

serving a tensorflow classifier

I have been fighting with tensorflow's builder to be able to serve my model, I am trying to feed data to my classifier after serving the model
My question is how would i feed the input to the model?
I have seen the code used by google's inception tutorial
and have tried to implement it
classify_inputs_tensor_info = utils.build_tensor_info(
serialized_tf_example)
classes_output_tensor_info = utils.build_tensor_info(classes)
scores_output_tensor_info = utils.build_tensor_info(values)
classification_signature = signature_def_utils.build_signature_def(
inputs={
signature_constants.CLASSIFY_INPUTS: classify_inputs_tensor_info
},
outputs={
signature_constants.CLASSIFY_OUTPUT_CLASSES:
classes_output_tensor_info,
signature_constants.CLASSIFY_OUTPUT_SCORES:
scores_output_tensor_info
},
method_name=signature_constants.CLASSIFY_METHOD_NAME)
and from what i understand the input is passed to a tensor called serialized_tf_example which as the name suggests serializes the input to string but then they use tf.FixedLenFeature which i don't understand and then parses the serialized_tf_example with tf.parse_example and assigns it to x which is used within the model, but i would like to parse it to a classifier that accepts arrays as inputs but don't know how to go around this.
while trying to implement this i wrote this
serialized_tf_example = tf.placeholder(tf.string, name='tf_example')
feature_configs = { 'audio/encoded': tf.FixedLenFeature( shape=[193], dtype=tf.float32, default_value=input_x),}
tf_example = tf.parse_example(serialized_tf_example, feature_configs)
x = tf_example['audio/encoded']
sess = tf.InteractiveSession()
sess.run(tf.global_variables_initializer())
# Define the dimensions in the feature columns
feature_columns = [tf.contrib.layers.real_valued_column("", dimension=5)]
classifier = tf.contrib.learn.DNNLinearCombinedClassifier(
dnn_feature_columns=feature_columns, dnn_hidden_units=[200,300], n_classes=10,
dnn_optimizer=tf.train.GradientDescentOptimizer(
learning_rate=0.01
)
)
#run training
classifier.fit(input_fn=get_train_inputs, steps=100)
#testing
accuracy_score = classifier.evaluate(input_fn=get_test_inputs, steps=10)["accuracy"]
print('Test accuracy : ', format(accuracy_score))
prediction = format(list(classifier.predict_classes(x, as_iterable=True)))
but x is a tensor and so is not able to be read. when i try use run or .eval() it asks me to feed a value to serialized_tf_example
InvalidArgumentError (see above for traceback): You must feed a value
for placeholder tensor 'tf_example' with dtype string [[Node:
tf_example = Placeholderdtype=DT_STRING, shape=[],
_device="/job:localhost/replica:0/task:0/cpu:0"]]
when i use prediction = format(list(classifier.predict_classes(np.array(x), as_iterable=True))
I get
InvalidArgumentError (see above for traceback): Shape in
shape_and_slice spec [1,200] does not match the shape stored in
checkpoint: [193,200] [[Node: save/RestoreV2_1 =
RestoreV2[dtypes=[DT_FLOAT],
_device="/job:localhost/replica:0/task:0/cpu:0"](_recv_save/Const_0, save/RestoreV2_1/tensor_names, save/RestoreV2_1/shape_and_slices)]]

You can/should use classifier.predict without tf.Example.Your input_fn in train and eval returns x, y. you can write a predict_input_fn similar to other input functions.
predictoin = next(classifier.predict_classes(input_fn=predict_input_fn))
Please note that, if you get all predictions with list function should end by an exception. You can check tf.estimator.inputs.numpy_input_fn