This is my first experience with Tensorflow. There appears to be many queries to this ValueError, however I am not getting any relief. I am using the notMNIST dataset, which is split 70/30 train test.
The error message appears to suggest there is a problem with my mini-batch. I have printed the shape of the placeholders, reshaped the input and label data to no success.
import tensorflow as tf
tf.reset_default_graph()
num_inputs = 28*28 # Size of images in pixels
num_hidden1 = 500
num_hidden2 = 500
num_outputs = len(np.unique(y)) # Number of classes (labels)
learning_rate = 0.0011
inputs = tf.placeholder(tf.float32, shape=[None, num_inputs], name="x")
labels = tf.placeholder(tf.int32, shape=[None], name = "y")
print(np.expand_dims(inputs, axis=0))
print(np.expand_dims(labels, axis=0))
def neuron_layer(x, num_neurons, name, activation=None):
with tf.name_scope(name):
num_inputs = int(x.get_shape()[1])
stddev = 2 / np.sqrt(num_inputs)
init = tf.truncated_normal([num_inputs, num_neurons], stddev=stddev)
W = tf.Variable(init, name = "weights")
b = tf.Variable(tf.zeros([num_neurons]), name= "biases")
z = tf.matmul(x, W) + b
if activation == "sigmoid":
return tf.sigmoid(z)
elif activation == "relu":
return tf.nn.relu(z)
else:
return z
with tf.name_scope("dnn"):
hidden1 = neuron_layer(inputs, num_hidden1, "hidden1", activation="relu")
hidden2 = neuron_layer(hidden1, num_hidden2, "hidden2", activation="relu")
logits = neuron_layer(hidden2, num_outputs, "output")
with tf.name_scope("loss"):
xentropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=labels, logits=logits)
loss = tf.reduce_mean(xentropy, name="loss")
with tf.name_scope("evaluation"):
correct = tf.nn.in_top_k(logits, labels, 1)
accuracy = tf.reduce_mean(tf.cast(correct, tf.float32))
with tf.name_scope("train"):
optimizer = tf.train.GradientDescentOptimizer(learning_rate)
grads = optimizer.compute_gradients(loss)
training_op = optimizer.apply_gradients(grads)
for var in tf.trainable_variables():
tf.summary.histogram(var.op.name + "/values", var)
for grad, var in grads:
if grad is not None:
tf.summary.histogram(var.op.name + "/gradients", grad)
# summary
accuracy_summary = tf.summary.scalar('accuracy', accuracy)
# merge all summary
tf.summary.histogram('hidden1/activations', hidden1)
tf.summary.histogram('hidden2/activations', hidden2)
merged = tf.summary.merge_all()
init = tf.global_variables_initializer()
saver = tf.train.Saver()
from datetime import datetime
now = datetime.utcnow().strftime("%Y%m%d%H%M%S")
root_logdir = "tf_logs/example03/dnn_final"
logdir = "{}/run-{}/".format(root_logdir, now)
train_writer = tf.summary.FileWriter("models/dnn0/train",
tf.get_default_graph())
test_writer = tf.summary.FileWriter("models/dnn0/test", tf.get_default_graph())
num_epochs = 50
batch_size = 128
with tf.Session() as sess:
init.run()
print("Epoch\tTrain accuracy\tTest accuracy")
for epoch in range(num_epochs):
for idx_start in range(0, x_train.shape[0], batch_size):
idx_end = num_epochs
x_batch, y_batch = x_train[batch_size], y_train[batch_size]
sess.run(training_op, feed_dict={inputs: x_batch, labels: y_batch})
summary_train, acc_train = sess.run([merged, accuracy],
feed_dict={x: x_batch, y: y_batch})
summary_test, acc_test = sess.run([accuracy_summary, accuracy],
feed_dict={x: x_test, y: y_test})
train_writer.add_summary(summary_train, epoch)
test_writer.add_summary(summary_test, epoch)
print("{}\t{}\t{}".format(epoch, acc_train, acc_test))
save_path = saver.save(sess, "models/dnn0.ckpt")
The following error
ValueError: Cannot feed value of shape (784,) for Tensor 'x:0', which has shape '(?, 784)'
occurs in line 96
sess.run(training_op, feed_dict={inputs: x_batch, labels: y_batch})
Your tensors do have mixed up shapes. You feed a tensor where the batch index is at the end into a tensor where the batch index is at the front.
Do x_batch = numpy.swapaxes(x_batch, 1, 0) before feeding the tensor.
On this line, you're referring to inputs and labels
sess.run(training_op, feed_dict={inputs: x_batch, labels: y_batch})
Where as on the lines below,
summary_train, acc_train = sess.run([merged, accuracy],
feed_dict={x: x_batch, y: y_batch})
summary_test, acc_test = sess.run([accuracy_summary, accuracy],
feed_dict={x: x_test, y: y_test})
you're referring to x and y. Change these to be the same. I.e. it should be the same value as your placeholder variables. (inputs and labels)
Related
I am trying to build Graph Convolutional Network. I converted my dataframe to PyTorch
required format using below code.
class S_Dataset(Dataset):
def __init__(self, df, transform=None):
self.df = df
self.transform = transform
def __len__(self):
return len(self.df)
def __getitem__(self, idx):
row = self.df.iloc[idx]
x = torch.tensor([row.date.to_pydatetime().timestamp(), row.s1, row.s2, row.s3, row.s4, row.temp ,row.rh, row.Location, row.Node ], dtype=torch.float)
y = torch.tensor([row.Location], dtype=torch.long)
weight1 = torch.tensor([row.neighbor1_distance], dtype=torch.float)
weight2 = torch.tensor([row.neighbor2_distance], dtype=torch.float)
weight3 = torch.tensor([row.neighbor3_distance], dtype=torch.float)
edge_index1 = torch.tensor([[row.Location, row.neighbor1_name]], dtype=torch.long).t()
edge_index2 = torch.tensor([[row.Location, row.neighbor2_name]], dtype=torch.long).t()
edge_index3 = torch.tensor([[row.Location, row.neighbor3_name]], dtype=torch.long).t()
edge_index = torch.cat([edge_index1, edge_index2, edge_index3 ], dim=1)
weight = torch.cat([weight1, weight2, weight3], dim=0)
if self.transform:
x, y, edge_index, weight = self.transform(x, y, edge_index, weight)
return x, y, edge_index, weight
Process_Data = S_Dataset(df)
Next I divided data into train and test set:
train_size = int(len(Process_Data) * 0.8)
test_size = len(Process_Data) - train_size
train_dataset, test_dataset = torch.utils.data.random_split(Process_Data, [train_size, test_size])
# Create dataloaders
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=32, shuffle=True )
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=32, shuffle=True )
I designed a simple model:
import torch
import torch.nn as nn
import torch.optim as optim
from torch_geometric.nn import GCNConv
# Create the model
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = GCNConv(9, 128)
self.conv2 = GCNConv(128, 64)
self.fc1 = nn.Linear(64, 32)
self.fc2 = nn.Linear(32, len(location_to_id))
def forward(self, x, edge_index, weight):
x = self.conv1(x, edge_index, weight)
x = torch.relu(x)
x = self.conv2(x, edge_index, weight)
x = torch.relu(x)
x = x.view(-1, 64)
x = self.fc1(x)
x = torch.relu(x)
x = self.fc2(x)
return x
Finally to train the model:
model = Net()
optimizer = optim.Adam(model.parameters(), lr=0.01)
criterion = nn.CrossEntropyLoss()
for epoch in range(100):
total_loss = 0
for batch in train_loader:
optimizer.zero_grad()
x, y, edge_index, weight = batch
y_pred = model(x, edge_index, weight)
loss = criterion(y_pred, y)
loss.backward()
optimizer.step()
total_loss += loss.item()
print('Epoch: {} Loss: {:.4f}'.format(epoch, total_loss / len(train_loader)))
I am facing following error:
IndexError: The shape of the mask [2, 3] at index 0 does not match the shape of the indexed tensor [32, 3] at index 0
x, y, edge_index, weight = batch
This line is causing error.
How can I resphae my data so I can train my model?
The batch size is set at 32, but there might not be enough samples to fit in the batch size of 32.
I am assuming, this error occurs after the code runs for some time, I would appreciate more context on the problem
A general solution could be decreasing the size of batch to something smaller and trying the code again. Making sure all samples are covered in the epoch.
I am trying to implement tensorflow regression model ,my data shape is train_X=(200,4) and train_Y=(200,). i am getting shape error ,here is my piece of code please can anyone mention where i am doing mistake.
df=pd.read_csv('all.csv')
df=df.drop('Time',axis=1)
print(df.describe()) #to understand the dataset
train_Y=df["power"]
train_X=df.drop('power',axis=1)
train_X=numpy.asarray(train_X)
train_Y=numpy.asarray(train_Y)
n_samples = train_X.shape[0]
tf Graph Input
X = tf.placeholder('float',[None,len(train_X[0])])
Y = tf.placeholder("float")
Set model weights
W = tf.Variable(rng.randn(), name="weight")
b = tf.Variable(rng.randn(), name="bias")
Construct a linear model
pred = tf.add(tf.multiply(X, W), b)
Mean squared error
cost = tf.reduce_sum(tf.pow(pred-Y, 2))/(2*n_samples)
Gradient descent
Note, minimize() knows to modify W and b because Variable objects are
trainable=True by default
optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)
Initialize the variables (i.e. assign their default value)
init = tf.global_variables_initializer()
Start training
with tf.Session() as sess:
# Run the initializer
sess.run(init)
# Fit all training data
for epoch in range(training_epochs):
for (x, y) in zip(train_X, train_Y):
sess.run(optimizer, feed_dict={X: x, Y: y})
# Display logs per epoch step
if (epoch+1) % display_step == 0:
c = sess.run(cost, feed_dict={X: train_X, Y:train_Y})
print("Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(c), \
"W=", sess.run(W), "b=", sess.run(b))
print("Optimization Finished!")
training_cost = sess.run(cost, feed_dict={X: train_X, Y: train_Y})
print("Training cost=", training_cost, "W=", sess.run(W), "b=", sess.run(b), '\n')
# Graphic display
plt.plot(train_X, train_Y, 'ro', label='Original data')
plt.plot(train_X, sess.run(W) * train_X + sess.run(b), label='Fitted line')
plt.legend()
plt.show()enter code here
i changed shape and problem solved
train_y = np.reshape(train_y, (-1, 1))
I'm trying to build a deep neural network model with 3 hidden layers using TensorFlow. But I've encountered an error VarianceScaling' object is not subscriptable on this line:
W_hidden_1 = tf.Variable(weight_initializer[n_input, n_hl1])
Below is my code:
n_input = 18
n_target = 1
n_hl1 = 10
n_hl2 = 10
n_hl3 = 10
learning_rate = 0.1
batch_size = 100
X = tf.placeholder('float')
Y = tf.placeholder('float')
# Initializers
sigma = 1
weight_initializer = tf.variance_scaling_initializer(mode="fan_avg", distribution="uniform", scale=sigma)
bias_initializer = tf.zeros_initializer()
# Layer 1: Variables for hidden weights and biases
W_hidden_1 = tf.Variable(weight_initializer[n_input, n_hl1])
bias_hidden_1 = tf.Variable(bias_initializer([n_hl1]))
# Layer 2: Variables for hidden weights and biases
W_hidden_2 = tf.Variable(weight_initializer([n_hl1, n_hl2]))
bias_hidden_2 = tf.Variable(bias_initializer([n_hl2]))
# Layer 3: Variables for hidden weights and biases
W_hidden_3 = tf.Variable(weight_initializer([n_hl2, n_hl3]))
bias_hidden_3 = tf.Variable(bias_initializer([n_hl3]))
# Output layer: Variables for output weights and biases
W_out = tf.Variable(weight_initializer([n_hl3, n_target]))
bias_out = tf.Variable(bias_initializer([n_target]))
# Hidden layer
hidden_1 = tf.nn.relu(tf.add(tf.matmul(X, W_hidden_1), bias_hidden_1))
hidden_2 = tf.nn.relu(tf.add(tf.matmul(hidden_1, W_hidden_2), bias_hidden_2))
hidden_3 = tf.nn.relu(tf.add(tf.matmul(hidden_2, W_hidden_3), bias_hidden_3))
# Output layer (must be transposed)
out = tf.transpose(tf.add(tf.matmul(hidden_3, W_out), bias_out))
#prediction = neural_network_model(x)
cost =tf.reduce_mean(tf.squared_difference(out, Y))
optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)
epochs = 1000
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for e in range(epochs):
# Shuffle training data
shuffle_indices = np.random.permutation(np.arange(len(y_data)))
x_data = x_data[shuffle_indices]
y_data = y_data[shuffle_indices]
# Minibatch training
for i in range(0, len(y_data) // batch_size):
start = i * batch_size
batch_x = x_data[start:start + batch_size]
batch_y = y_data[start:start + batch_size]
# Run optimizer with batch
sess.run(optimizer, feed_dict={X: batch_x, Y: batch_y})
mse_final = sess.run(cost, feed_dict={X: x_test, Y: y_test})
print(mse_final)
Any help is appreciated. :)
This is because you need to insert parentheses as follows,
W_hidden_1 = tf.Variable(weight_initializer([n_input, n_hl1])).
and not
W_hidden_1 = tf.Variable(weight_initializer[n_input, n_hl1])
Otherwise, python thinks that you are trying to access tf.variance_scaling_initializer.
Hope this helps.
I build a Neural Network with two hidden layer.
from collections import namedtuple
def multilayer_perceptron():
tf.reset_default_graph()
inputs = tf.placeholder(tf.float32, shape=[None,train_x.shape[1]])
y = tf.placeholder(tf.float32, shape=[None, 1])
weights = {
'h1': tf.Variable(tf.random_normal([train_x.shape[1], n_hidden_1])),
'h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])),
'out': tf.Variable(tf.random_normal([n_hidden_2, 1]))
}
biases = {
'b1': tf.Variable(tf.random_normal([n_hidden_1])),
'b2': tf.Variable(tf.random_normal([n_hidden_2])),
'out': tf.Variable(tf.random_normal([1]))
}
# Hidden layer con funzione di attivazione ReLU
layer_1 = tf.add(tf.matmul(inputs, weights['h1']), biases['b1'])
layer_1 = tf.nn.relu(layer_1)
# Hidden layer with ReLU activation
layer_2 = tf.add(tf.matmul(layer_1, weights['h2']), biases['b2'])
layer_2 = tf.nn.relu(layer_2)
# Output layer with linear activation
out_layer = tf.matmul(layer_2, weights['out']) + biases['out']
learning_rate = tf.placeholder(tf.float32)
is_training=tf.Variable(True,dtype=tf.bool)
cross_entropy = tf.nn.sigmoid_cross_entropy_with_logits(labels=y,logits=out_layer )
cost = tf.reduce_mean(cross_entropy)
with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)
predicted = tf.nn.sigmoid(out_layer)
correct_pred = tf.equal(tf.round(predicted), y)
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
# Export the nodes
export_nodes = ['inputs', 'y', 'learning_rate','is_training', 'out_layer',
'cost', 'optimizer', 'predicted', 'accuracy']
Graph = namedtuple('Graph', export_nodes)
local_dict = locals()
graph = Graph(*[local_dict[each] for each in export_nodes])
return graph
pred1 = multilayer_perceptron()
Next I create a function for determinate the batch for input and output value:
def get_batch(data_x,data_y,batch_size=32):
batch_n=len(data_x)//batch_size
for i in range(batch_n):
batch_x=data_x[i*batch_size:(i+1)*batch_size]
batch_y=data_y[i*batch_size:(i+1)*batch_size]
yield batch_x,batch_y
epochs = 25
train_collect = 20
train_print=train_collect*2
learning_rate_value = 0.001
batch_size=400
x_collect = []
train_loss_collect = []
train_acc_collect = []
valid_loss_collect = []
valid_acc_collect = []
saver = tf.train.Saver()
Finally I launch the session that print a Loss function and accuracy of train model. I save the result on file .ckpt
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
iteration=0
for e in range(epochs):
for batch_x,batch_y in get_batch(train_x,train_y,batch_size):
iteration+=1
feed = {pred1.inputs: train_x,
pred1.y: train_y,
pred1.learning_rate: learning_rate_value,
pred1.is_training:True
}
train_loss, _, train_acc = sess.run([pred1.cost, pred1.optimizer, pred1.accuracy], feed_dict=feed)
if iteration % train_collect == 0:
x_collect.append(e)
train_loss_collect.append(train_loss)
train_acc_collect.append(train_acc)
if iteration % train_print==0:
print("Epoch: {}/{}".format(e + 1, epochs),
"Train Loss: {:.4f}".format(train_loss),
"Train Acc: {:.4f}".format(train_acc))
feed = {pred1.inputs: valid_x,
pred1.y: valid_y,
pred1.is_training:False
}
val_loss, val_acc = sess.run([pred1.cost, pred1.accuracy], feed_dict=feed)
valid_loss_collect.append(val_loss)
valid_acc_collect.append(val_acc)
if iteration % train_print==0:
print("Epoch: {}/{}".format(e + 1, epochs),
"Validation Loss: {:.4f}".format(val_loss),
"Validation Acc: {:.4f}".format(val_acc))
saver.save(sess, "./insurance2.ckpt")
When I launch the session for data set the code give me an error:
model=multilayer_perceptron()
restorer=tf.train.Saver()
with tf.Session() as sess:
restorer.restore(sess,"./insurance2.ckpt")
feed={
pred1.inputs:test_data,
pred1.is_training:False
}
test_predict=sess.run(pred1.predicted,feed_dict=feed)
The two error are:
ValueError: Tensor Tensor("Placeholder:0", shape=(?, 125), dtype=float32) is not an element of this graph.
TypeError: Cannot interpret feed_dict key as Tensor: Tensor Tensor("Placeholder:0", shape=(?, 125), dtype=float32) is not an element of this graph.
In my graph I export input.
I'm working on creating an image classifier that can differentiate between cats and dogs. I have the follwing code:
import cv2
import os
from tqdm import tqdm
import numpy as np
import tensorflow as tf
img_height = 128
img_width = 128
path = "./train"
# class info
file = os.listdir(path)
index = []
images = []
# image size and channels
channels = 3
n_inputs = img_width * img_height * channels
# First convolutional layer
conv1_fmaps = 96 # Number of feature maps created by this layer
conv1_ksize = 4 # kernel size 3x3
conv1_stride = 2
conv1_pad = "SAME"
# Second convolutional layer
conv2_fmaps = 192
conv2_ksize = 4
conv2_stride = 4
conv2_pad = "SAME"
# Third layer is a pooling layer
pool3_fmaps = conv2_fmaps # Isn't it obvious?
n_fc1 = 192 # Total number of output features
n_outputs = 2
with tf.name_scope("inputs"):
X = tf.placeholder(tf.float32, shape=[None, img_width, img_height, channels], name="X")
X_reshaped = tf.reshape(X, shape=[-1, img_height, img_width, channels])
y = tf.placeholder(tf.int32, shape=[None, 2], name="y")
conv1 = tf.layers.conv2d(X_reshaped, filters=conv1_fmaps, kernel_size=conv1_ksize, strides=conv1_stride, padding=conv1_pad, activation=tf.nn.relu, name="conv1")
conv2 = tf.layers.conv2d(conv1, filters=conv2_fmaps, kernel_size=conv2_ksize, strides=conv2_stride, padding=conv2_pad, activation=tf.nn.relu, name="conv2")
n_epochs = 10
batch_size = 250
with tf.name_scope("pool3"):
pool3 = tf.nn.max_pool(conv2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding="VALID")
pool3_flat = tf.reshape(pool3, shape=[-1, pool3_fmaps * 8 * 8])
with tf.name_scope("fc1"):
fc1 = tf.layers.dense(pool3_flat, n_fc1, activation=tf.nn.relu name="fc1")
with tf.name_scope("output"):
logits = tf.layers.dense(fc1, n_outputs, name="output")
Y_proba = tf.nn.softmax(logits, name="Y_proba")
with tf.name_scope("train"):
xentropy=tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=y)
loss = tf.reduce_mean(xentropy)
optimizer = tf.train.AdamOptimizer()
training_op = optimizer.minimize(loss)
with tf.name_scope("eval"):
correct = tf.nn.in_top_k(logits, y, 1)
accuracy = tf.reduce_mean(tf.cast(correct, tf.float32))
init = tf.global_variables_initializer()
with tf.name_scope("init_and_save"):
saver = tf.train.Saver()
def next_batch(num):
index = []
images = []
# Data set Creation
print("Creating batch dataset "+str(num+1)+"...")
for f in tqdm(range(num * batch_size, (num+1)*batch_size)):
if file[f].find("dog"):
index.append(np.array([0, 1]))
else:
index.append(np.array([1, 0]))
image = cv2.imread(path + "/" + file[f])
image = cv2.resize(image, (img_width, img_height), 0, 0, cv2.INTER_LINEAR)
# image = image.astype(np.float32)
images.append(image)
images = np.array(images, dtype=np.uint8)
images = images.astype('float32')
images = images / 255
print("\nBatch "+str(num+1)+" creation finished.")
# print([images, index])
return [images, index]
with tf.Session() as sess:
init.run()
for epoch in range(n_epochs):
for iteration in range(25000 // batch_size):
X_batch, y_batch = next_batch(iteration)
sess.run(training_op, feed_dict={X: X_batch, y: y_batch})
acc_train = accuracy.eval(feed_dict={X: X_batch, y: y_batch})
print(epoch, "Train accuracy:", acc_train)
save_path = saver.save(sess, "./dogvscat_mnist_model.ckpt")
But I'm getting this error:
ValueError: Rank mismatch: Rank of labels (received 2) should equal rank of logits minus 1 (received 2).
Can anyone point out the problem and help me to solve it. I'm totally new to this.
For tf.nn.sparse_softmax_corss_entropy_with_logits rank(labels) = rank(logits) - 1, so you need to redefine the labels placeholder as follows
...
y = tf.placeholder(tf.int32, shape=[None], name="y")
...
xentropy=tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits,labels=y)
...
X_batch, y_batch = next_batch(iteration)
y_batch = np.argmax(y_batch, axis=1)
OR you can you just use tf.nn.softmax_cross_entropy_with_logits without changing labels placeholder.
xentropy=tf.nn.softmax_cross_entropy_with_logits(logits=logits,labels=y)