I am using Windows 10 pro, python 3.6.2rc1, Visual Studio 2017, and Tensorflow. I am working with Tensorflow example in its tutorial in the following link:
https://www.tensorflow.org/tutorials/layers
I have added another layer of convolution and pooling before flattening the last layer (3rd layer) to see if the accuracy changes.
The code I have added is as follows:
## Input Tensor Shape: [batch_size, 7, 7, 64]
## Output Tensor Shape: [batch_size, 7, 7, 64]
conv3 = tf.layers.conv2d(
inputs=pool2,
filters=64,
kernel_size=[3, 3],
padding=1,
activation=tf.nn.relu)
pool3 = tf.layers.max_pooling2d(inputs=conv3, pool_size=[2, 2], strides=1)
pool3_flat = tf.reshape(pool3, [-1, 7* 7 * 64])
The reason I have changed padding to 1 and stride to 1 is to make sure the size of output is the same as input. But after adding this new layer I get the following warnings and without showing any result the program ends:
Estimator is decoupled from Scikit Learn interface by moving into
separate class SKCompat. Arguments x, y and batch_size are only
available in the SKCompat class, Estimator will only accept input_fn.
Example conversion:
est = Estimator(...) -> est = SKCompat(Estimator(...))
WARNING:tensorflow:From E:\Apps\DA2CNNTest\TFHWDetection WIth More Layers\TFClassification\TFClassification\TFClassification.py:179: calling BaseEstimator.fit (from tensorflow.contrib.learn.python.learn.estimators.estimator) with batch_size is deprecated and will be removed after 2016-12-01.
Instructions for updating:
Estimator is decoupled from Scikit Learn interface by moving into
separate class SKCompat. Arguments x, y and batch_size are only
available in the SKCompat class, Estimator will only accept input_fn.
Example conversion:
est = Estimator(...) -> est = SKCompat(Estimator(...))
The thread 'MainThread' (0x5c8) has exited with code 0 (0x0).
The program '[13468] python.exe' has exited with code 1 (0x1).
Without adding this layer it works properly. In order to solve this problem I changed the conv3 and pool3 as follows:
conv3 = tf.layers.conv2d(
inputs=pool2,
filters=64,
kernel_size=[5, 5],
padding="same",
activation=tf.nn.relu)
# Input Tensor Shape: [batch_size, 7, 7, 64]
# Output Tensor Shape: [batch_size, 3, 3, 64]
pool3 = tf.layers.max_pooling2d(inputs=conv3, pool_size=[2, 2], strides=2)
pool3_flat = tf.reshape(pool3, [-1, 3* 3 * 64])
but then I got a different error at
nist_classifier.fit(
x=train_data,
y=train_labels,
batch_size=100,
steps=20000,
monitors=[logging_hook])
which is as follows:
tensorflow.python.framework.errors_impl.NotFoundError: Key conv2d_2/bias not found in checkpoint
[[Node: save/RestoreV2_5 = RestoreV2[dtypes=[DT_FLOAT], _device="/job:localhost/replica:0/task:0/cpu:0"](_arg_save/Const_0_0, save/RestoreV2_5/tensor_names, save/RestoreV2_5/shape_and_slices)]]
The error is exactly refering to monitors=[logging_hook].
My whole code is as follow and as you see I have commented the previous one with padding=1.
I really appreciate if you can guide me what my mistake is and why is it so. Moreover, I am correct with the dimension of my inputs and outputs in the 3rd layer?
Complete code:
"""Convolutional Neural Network Estimator for MNIST, built with tf.layers."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import tensorflow as tf
from tensorflow.contrib import learn
from tensorflow.contrib.learn.python.learn.estimators import model_fn as model_fn_lib
tf.logging.set_verbosity(tf.logging.INFO)
def cnn_model_fn(features, labels, mode):
"""Model function for CNN."""
input_layer = tf.reshape(features, [-1, 28, 28, 1])
# Input Tensor Shape: [batch_size, 28, 28, 1]
# Output Tensor Shape: [batch_size, 28, 28, 32]
conv1 = tf.layers.conv2d(
inputs=input_layer,
filters=32,
kernel_size=[5, 5],
padding="same",
activation=tf.nn.relu)
# Input Tensor Shape: [batch_size, 28, 28, 32]
# Output Tensor Shape: [batch_size, 14, 14, 32]
pool1 = tf.layers.max_pooling2d(inputs=conv1, pool_size=[2, 2], strides=2)
# Convolutional Layer #2
# Input Tensor Shape: [batch_size, 14, 14, 32]
# Output Tensor Shape: [batch_size, 14, 14, 64]
conv2 = tf.layers.conv2d(
inputs=pool1,
filters=64,
kernel_size=[5, 5],
padding="same",
activation=tf.nn.relu)
# Pooling Layer #2
# Input Tensor Shape: [batch_size, 14, 14, 64]
# Output Tensor Shape: [batch_size, 7, 7, 64]
pool2 = tf.layers.max_pooling2d(inputs=conv2, pool_size=[2, 2], strides=2)
'''Adding a new layer of conv and pool'''
## Input Tensor Shape: [batch_size, 7, 7, 32]
## Output Tensor Shape: [batch_size, 7, 7, 64]
#conv3 = tf.layers.conv2d(
# inputs=pool2,
# filters=64,
# kernel_size=[3, 3],
# padding=1,
# activation=tf.nn.relu)
## Input Tensor Shape: [batch_size, 7, 7, 64]
## Output Tensor Shape: [batch_size, 7, 7, 64]
#pool3 = tf.layers.max_pooling2d(inputs=conv3, pool_size=[2, 2], strides=1)
#pool3_flat = tf.reshape(pool3, [-1, 7* 7 * 64])
# Input Tensor Shape: [batch_size, 7, 7, 64]
# Output Tensor Shape: [batch_size, 7, 7, 64]
conv3 = tf.layers.conv2d(
inputs=pool2,
filters=64,
kernel_size=[5, 5],
padding="same",
activation=tf.nn.relu)
# Input Tensor Shape: [batch_size, 7, 7, 64]
# Output Tensor Shape: [batch_size, 3, 3, 64]
pool3 = tf.layers.max_pooling2d(inputs=conv3, pool_size=[2, 2], strides=2)
'''End of manipulation'''
# Input Tensor Shape: [batch_size, 3, 3, 64]
# Output Tensor Shape: [batch_size, 3 * 3 * 64]
pool3_flat = tf.reshape(pool3, [-1, 3* 3 * 64])
# Input Tensor Shape: [batch_size, 3 * 3 * 64]
# Output Tensor Shape: [batch_size, 1024]
# dense(). Constructs a dense layer. Takes number of neurons and activation function as arguments.
dense = tf.layers.dense(inputs=pool3_flat, units=1024, activation=tf.nn.relu)
# Add dropout operation; 0.6 probability that element will be kept
dropout = tf.layers.dropout(
inputs=dense, rate=0.4, training=mode == learn.ModeKeys.TRAIN)
logits = tf.layers.dense(inputs=dropout, units=10)
loss = None
train_op = None
# Calculate Loss (for both TRAIN and EVAL modes)
if mode != learn.ModeKeys.INFER:
onehot_labels = tf.one_hot(indices=tf.cast(labels, tf.int32), depth=10)
loss = tf.losses.softmax_cross_entropy(
onehot_labels=onehot_labels, logits=logits)
# Configure the Training Op (for TRAIN mode)
if mode == learn.ModeKeys.TRAIN:
train_op = tf.contrib.layers.optimize_loss(
loss=loss,
global_step=tf.contrib.framework.get_global_step(),
learning_rate=0.001,
optimizer="SGD")
# Generate Predictions
# The logits layer of our model returns our predictions as raw values in a [batch_size, 10]-dimensional tensor.
predictions = {
"classes": tf.argmax(
input=logits, axis=1),
"probabilities": tf.nn.softmax(
logits, name="softmax_tensor")
}
# Return a ModelFnOps object
return model_fn_lib.ModelFnOps(
mode=mode, predictions=predictions, loss=loss, train_op=train_op)
def main(unused_argv):
# Load training and eval data
mnist = learn.datasets.load_dataset("mnist")
train_data = mnist.train.images # Returns np.array
train_labels = np.asarray(mnist.train.labels, dtype=np.int32)
eval_data = mnist.test.images # Returns np.array
eval_labels = np.asarray(mnist.test.labels, dtype=np.int32)
# Create the Estimator
mnist_classifier = learn.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
mnist_classifier.fit(
x=train_data,
y=train_labels,
batch_size=100,
steps=20000,
monitors=[logging_hook])
# Configure the accuracy metric for evaluation
#change metrics variable name
metricss = {
"accuracy":
learn.MetricSpec(
metric_fn=tf.metrics.accuracy, prediction_key="classes"),
}
#Evaluate the model and print results
#for i in range(100)
eval_results = mnist_classifier.evaluate(
x=eval_data[0:100], y=eval_labels[0:100], metrics=metricss)
print(eval_results)
if __name__ == "__main__":
tf.app.run()
The error looks like the trained model which is available in the model_dir conflicts with the current graph changes. The Estimator loads checkpoints from the saved model directory and continue training from the previous saved model. So whenever your making changes in the model, you need to delete the old model and start training again.
A simple fix for this would be to define a custom checkpoint directory for the model as follows.
tf.train.generate_checkpoint_state_proto("/tmp/","/tmp/mnist_convnet_model")
This fixes the problem with the MNIST example and also gives you access to a location where you can control checkpoints.
Related
My Tensorflow convolutional layer has a shape I did not expect it to have and I do not see the mistake.
I am new to TensorFlow and want to use this function to create a convolutional layer:
def new_conv_layer(input, # The previous layer.
num_input_channels, # Num. channels in prev. layer.
filter_size, # Width and height of each filter.
num_filters, # Number of filters.
use_pooling=True): # Use 2x2 max-pooling.
shape = [filter_size, filter_size, num_input_channels, num_filters]
weights = new_weights(shape=shape)
biases = new_biases(length=num_filters)
layer = tf.nn.conv2d(input=input_,
filters=weights,
strides=[1, 1, 1, 1],
padding='SAME')
layer += biases
if use_pooling:
layer = tf.nn.max_pool(input=layer,
ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1],
padding='SAME')
layer = tf.nn.relu(layer)
return layer, weights
But when I use it with
num_channels = 1
img_size = 28
x_image = tf.reshape(x, [-1, img_size, img_size, num_channels])
# Convolutional Layer 1.
filter_size1 = 5 # Convolution filters are 5 x 5 pixels.
num_filters1 = 16 # There are 16 of these filters.
layer_conv1, weights_conv1 = new_conv_layer(input=x_image,
num_input_channels=num_channels,
filter_size=filter_size1,
num_filters=num_filters1,
use_pooling=True)
layer_conv1
I get this output:
<tf.Tensor 'Relu:0' shape=(None, 392, 392, 16) dtype=float32>
Because my images are of a square 28x28 shape and I apply 2x2 pooling, I would have expected this shape to be (None, 14, 14, 16).
Why is that not the case and how do I fix it?
in my case this line x = tf.compat.v1.placeholder(tf.float32, shape=[None, img_size_flat], name='x') was incorrect!
In particular img_size_flat was not the length of each "stretched" image, as it should have been.
img_size_flat = df.drop('label', axis=1).shape[1]
I am trying to implement the VGG but am getting the above odd error. I am running TFv2 on Ubuntu. Could this be because I am not running CUDA?
The code is from here.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
# Imports
import time
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
# tf.logging.set_verbosity(tf.logging.INFO)
from tensorflow.keras.layers import Conv2D, Dense, Flatten
np.random.seed(1)
mnist = tf.keras.datasets.mnist
(train_data, train_labels), (eval_data, eval_labels) = mnist.load_data()
train_data, train_labels = train_data / 255.0, train_labels / 255.0
# Add a channels dimension
train_data = train_data[..., tf.newaxis]
train_labels = train_labels[..., tf.newaxis]
index = 7
plt.imshow(train_data[index].reshape(28, 28))
plt.show()
time.sleep(5);
print("y = " + str(np.squeeze(train_labels[index])))
print ("number of training examples = " + str(train_data.shape[0]))
print ("number of evaluation examples = " + str(eval_data.shape[0]))
print ("X_train shape: " + str(train_data.shape))
print ("Y_train shape: " + str(train_labels.shape))
print ("X_test shape: " + str(eval_data.shape))
print ("Y_test shape: " + str(eval_labels.shape))
print("done")
def cnn_model_fn(features, labels, mode):
# Input Layer
input_height, input_width = 28, 28
input_channels = 1
input_layer = tf.reshape(features["x"], [-1, input_height, input_width, input_channels])
# Convolutional Layer #1 and Pooling Layer #1
conv1_1 = tf.layers.conv2d(inputs=input_layer, filters=64, kernel_size=[3, 3], padding="same",
activation=tf.nn.relu)
conv1_2 = tf.layers.conv2d(inputs=conv1_1, filters=64, kernel_size=[3, 3], padding="same", activation=tf.nn.relu)
pool1 = tf.layers.max_pooling2d(inputs=conv1_2, pool_size=[2, 2], strides=2, padding="same")
# Convolutional Layer #2 and Pooling Layer #2
conv2_1 = tf.layers.conv2d(inputs=pool1, filters=128, kernel_size=[3, 3], padding="same", activation=tf.nn.relu)
conv2_2 = tf.layers.conv2d(inputs=conv2_1, filters=128, kernel_size=[3, 3], padding="same", activation=tf.nn.relu)
pool2 = tf.layers.max_pooling2d(inputs=conv2_2, pool_size=[2, 2], strides=2, padding="same")
# Convolutional Layer #3 and Pooling Layer #3
conv3_1 = tf.layers.conv2d(inputs=pool2, filters=256, kernel_size=[3, 3], padding="same", activation=tf.nn.relu)
conv3_2 = tf.layers.conv2d(inputs=conv3_1, filters=256, kernel_size=[3, 3], padding="same", activation=tf.nn.relu)
pool3 = tf.layers.max_pooling2d(inputs=conv3_2, pool_size=[2, 2], strides=2, padding="same")
# Convolutional Layer #4 and Pooling Layer #4
conv4_1 = tf.layers.conv2d(inputs=pool3, filters=512, kernel_size=[3, 3], padding="same", activation=tf.nn.relu)
conv4_2 = tf.layers.conv2d(inputs=conv4_1, filters=512, kernel_size=[3, 3], padding="same", activation=tf.nn.relu)
pool4 = tf.layers.max_pooling2d(inputs=conv4_2, pool_size=[2, 2], strides=2, padding="same")
# Convolutional Layer #5 and Pooling Layer #5
conv5_1 = tf.layers.conv2d(inputs=pool4, filters=512, kernel_size=[3, 3], padding="same", activation=tf.nn.relu)
conv5_2 = tf.layers.conv2d(inputs=conv5_1, filters=512, kernel_size=[3, 3], padding="same", activation=tf.nn.relu)
pool5 = tf.layers.max_pooling2d(inputs=conv5_2, pool_size=[2, 2], strides=2, padding="same")
# FC Layers
pool5_flat = tf.contrib.layers.flatten(pool5)
FC1 = tf.layers.dense(inputs=pool5_flat, units=4096, activation=tf.nn.relu)
FC2 = tf.layers.dense(inputs=FC1, units=4096, activation=tf.nn.relu)
FC3 = tf.layers.dense(inputs=FC2, units=1000, activation=tf.nn.relu)
"""the training argument takes a boolean specifying whether or not the model is currently
being run in training mode; dropout will only be performed if training is true. here,
we check if the mode passed to our model function cnn_model_fn is train mode. """
dropout = tf.layers.dropout(inputs=FC3, rate=0.4, training=mode == tf.estimator.ModeKeys.TRAIN)
# Logits Layer or the output layer. which will return the raw values for our predictions.
# Like FC layer, logits layer is another dense layer. We leave the activation function empty
# so we can apply the softmax
logits = tf.layers.dense(inputs=dropout, units=10)
# Then we make predictions based on raw output
predictions = {
# Generate predictions (for PREDICT and EVAL mode)
# the predicted class for each example - a vlaue from 0-9
"classes": tf.argmax(input=logits, axis=1),
# to calculate the probablities for each target class we use the softmax
"probabilities": tf.nn.softmax(logits, name="softmax_tensor")
}
# so now our predictions are compiled in a dict object in python and using that we return an estimator object
if mode == tf.estimator.ModeKeys.PREDICT:
return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions)
'''Calculate Loss (for both TRAIN and EVAL modes): computes the softmax entropy loss.
This function both computes the softmax activation function as well as the resulting loss.'''
loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits)
# Configure the Training Options (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)
print("done2")
mnist_classifier = tf.estimator.Estimator(model_fn=cnn_model_fn,
model_dir="/tmp/mnist_vgg13_model")
print("done3")
train_input_fn = tf.compat.v1.estimator.inputs.numpy_input_fn(x={"x": train_data},
y=train_labels,
batch_size=100,
num_epochs=100,
shuffle=True)
print("done4")
mnist_classifier.train(input_fn=train_input_fn,
steps=None,
hooks=None)
print("done5")
eval_input_fn = tf.estimator.inputs.numpy_input_fn(x={"x": eval_data},
y=eval_labels,
num_epochs=1,
shuffle=False)
print("done6")
eval_results = mnist_classifier.evaluate(input_fn=eval_input_fn)
print(eval_results)
The code you're using was written in Tensorflow v1.x, and is not compatible as it is with Tensorflow v2. The easiest solution is probably to downgrade to a version of tensorflow v1 to run the code as it is.
An other option would be to could follow this guide to migrate the code from v1 to v2.
A third option would be to use the tf.compat module to get some retro-compatibility. For example, tf.layers does not exist anymore in Tensorflow v2. You can use tf.compat.v1.layers (see for example the Conv2D function) instead, but this is a temporary fix, as these functions will be removed in a future version.
You can use postfix compat.v1 to make code written for tensorflow 1.x work with newer versions.
In your case this can be achived by changing:
tf.layers.conv2d
to
tf.compat.v1.layers.conv2d
You can read more about migrating tensorflow v1.x to tensorflow v2.x here:
https://www.tensorflow.org/guide/migrate
Use tensorflow 1.x instead of tensorflow 2.x versions. But remember there is no 2.x version on Python 3.8. Use a lower version of Python which has tensorflow 1.x.
python3.6 -m pip install tensorflow==1.8.0
I am trying to train a model in TensorFlow. I've got a problem with the labels. Here is my input function:
def my_input_fn():
filenames = tf.constant(glob.glob("C:/test_proje/*.jpg"))
labels = tf.constant([0, 0, 1, 1, 1, 1, 1, 0, 0, 0])
labels = tf.one_hot(labels, 2)
dataset = tf.data.Dataset.from_tensor_slices((filenames, labels))
dataset = dataset.map(_parse_function)
return dataset
And here is the CNN model
def cnn_model_fn(features, labels, mode):
"""Model function for CNN."""
print(labels.shape)
print(labels[0])
# Input Layer
input_layer = tf.reshape(features["image"], [-1, 168, 84, 3])
# Convolutional Layer #1
conv1 = tf.layers.conv2d(
inputs=input_layer,
filters=32,
kernel_size=[5, 5],
padding="same",
activation=tf.nn.relu)
# Pooling Layer #1
pool1 = tf.layers.max_pooling2d(inputs=conv1, pool_size=[2, 2],
strides=2)
# Convolutional Layer #2 and Pooling Layer #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)
# Dense Layer
pool2_flat = tf.reshape(pool2, [-1, 42 * 21 * 64])
dense = tf.layers.dense(inputs=pool2_flat, units=4,
activation=tf.nn.relu)
dropout = tf.layers.dropout(
inputs=dense, rate=0.4, training=mode ==
tf.estimator.ModeKeys.TRAIN)
# Logits Layer
logits = tf.layers.dense(inputs=dropout, units=2)
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)
I am getting this error: ValueError: Shape mismatch: The shape of labels (received (2,)) should equal the shape of logits except for the last dimension (received (1, 2)).
When I print the shape of labels before cnn_model_fn, it is (10,2). But when I print it in cnn_model_fn, it suddenly becomes (2,)
Thanks.
I think it's your use of sparse_softmax_cross_entropy. You feed it one hot encoded labels which it doesn't want.
Switch to just normal softmax_cross_entropy and see if that works.
https://stackoverflow.com/a/37317322/7431458
I trained a CNN model successfully, however I am getting errors when I feed images to the model for it to predict the labels.
This is my model (I am restoring it with saver.restore)...
# load dataset
mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)
# interactive session
sess = tf.InteractiveSession()
# data and labels placeholder
x = tf.placeholder(tf.float32, shape=[None, 784])
y = tf.placeholder(tf.float32, shape=[None, 10])
# 32 filters of size 5x5 and 32 biases,
# the filters are used to create 32 feature maps
W_conv1 = weight_variable([5, 5, 1, 32])
b_conv1 = bias_variable([32])
x_img = tf.reshape(x, [-1, 28, 28, 1])
# first layer activated using a Relu activation function
conv1 = tf.nn.relu(conv2d(x_img, W_conv1) + b_conv1)
pool1 = max_pool_2x2(conv1)
# 64 filters of size 5x5
W_conv2 = weight_variable([5, 5, 32, 64])
b_conv2 = bias_variable([64])
# second layer
conv2 = tf.nn.relu(conv2d(pool1, W_conv2) + b_conv2)
pool2 = max_pool_2x2(conv2)
# fully connected layer with 1024 neurons
W_fully = weight_variable([7 * 7 * 64, 1024])
b_fully = bias_variable([1024])
pool2flat = tf.reshape(pool2, [-1, 7 * 7 * 64])
fully = tf.nn.relu(tf.matmul(pool2flat, W_fully) + b_fully)
# dropout layer removes dead neurons
prob_drop = tf.placeholder(tf.float32)
dropout = tf.nn.dropout(fully, prob_drop)
# readout layer that will return the raw values
# of our predictions
W_readout = weight_variable([1024, 10])
b_readout = bias_variable([10])
y_conv = tf.matmul(dropout, W_readout) + b_readout
# loss function
cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=y_conv, labels=y))
# restore the trained CNN model
saver = tf.train.Saver()
saver.restore(sess, "/tmp/model2.ckpt")
y_conv is the predictor.
The model is trained on the mnist dataset, now I have an image of a number and I want the model to tell me what it thinks it is in terms of accuracy. I tried the following...
prediction = tf.argmax(y_conv, 1)
print(sess.run(prediction, feed_dict={x:two_images[0]}))
After feeding the image two_images[0] to the model, I got the following error...
ValueError: Cannot feed value of shape (784,) for Tensor 'Placeholder:0', which has shape '(?, 784)'
So I fixed it by doing the following...
prediction = tf.argmax(y_conv, 1)
print(sess.run(prediction, feed_dict={x:two_images[0].reshape((1, 784))}))
But now I am getting a whole bunch of errors that I cannot decipher...
InvalidArgumentError (see above for traceback): You must feed a value for placeholder tensor 'Placeholder_2' with dtype float
[[Node: Placeholder_2 = Placeholderdtype=DT_FLOAT, shape=, _device="/job:localhost/replica:0/task:0/device:CPU:0"]]
I am not sure what I am doing wrong.
EDIT
This is how I populate the variable two_images...
# extract the indices of the number 2
two_idxs_list = np.where(mnist.test.labels[:, 2].astype(int) == 1)
two_idxs = two_idxs_list[0][:10]
# use the indices to extract the images of 2 and their corresponding label
two_images = mnist.test.images[two_idxs]
two_labels = mnist.test.labels[two_idxs]
Okay with the code added I was able to test in on my machine. The issue is that your network expects two inputs, one image, and a label. Even if you only do inference, you have to supply an input, maybe just some zeroes? Obviously the loss calculation will be wrong, but you're not interested in that, only in the prediction. So your sess.run line should be:
print( sess.run( prediction, feed_dict= {
x: two_images[0].reshape((1, 784)),
y: np.zeros( shape = ( 1, 10 ), dtype = np.float32 ) } ) )
I'm trying to replicate the CNN described in
https://pdfs.semanticscholar.org/3b57/85ca3c29c963ae396c2f94ba1a805c787cc8.pdf
and I'm stuck at the last layer. I've modeled the cnn like this
# Model function for CNN
def cnn_model_fn(features, labels, mode):
# Input Layer
# Reshape X to 4-D tensor: [batch_size, width, height, channels]
# Taxes images are 150x150 pixels, and have one color channel
input_layer = tf.reshape(features, [-1, 150, 150, 1])
# Convolutional Layer #1
# Input Tensor Shape: [batch_size, 150, 150, 1]
# Output Tensor Shape: [batch_size, 144, 144, 20]
conv1 = tf.layers.conv2d(
inputs=input_layer,
filters=20,
kernel_size=[7, 7],
padding="valid",
activation=tf.nn.relu)
# Pooling Layer #1
# Input Tensor Shape: [batch_size, 144, 144, 20]
# Output Tensor Shape: [batch_size, 36, 36, 20]
pool1 = tf.layers.max_pooling2d(inputs=conv1, pool_size=[4, 4], strides=4)
# Convolutional Layer #2
# Input Tensor Shape: [batch_size, 36, 36, 20]
# Output Tensor Shape: [batch_size, 32, 32, 50]
conv2 = tf.layers.conv2d(
inputs=pool1,
filters=50,
kernel_size=[5, 5],
padding="valid",
activation=tf.nn.relu)
# Pooling Layer #2
# Input Tensor Shape: [batch_size, 32, 32, 50]
# Output Tensor Shape: [batch_size, 8, 8, 50]
pool2 = tf.layers.max_pooling2d(inputs=conv2, pool_size=[4, 4], strides=4)
# Flatten tensor into a batch of vectors
# Input Tensor Shape: [batch_size, 8, 8, 50]
# Output Tensor Shape: [batch_size, 8 * 8 * 50]
pool2_flat = tf.reshape(pool2, [-1, 8 * 8 * 50])
# Dense Layer #1
# Densely connected layer with 1000 neurons
# Input Tensor Shape: [batch_size, 8 * 8 * 50]
# Output Tensor Shape: [batch_size, 1000]
dense1 = tf.layers.dense(inputs=pool2_flat, units=1000, activation=tf.nn.relu)
# Dense Layer #2
# Densely connected layer with 1000 neurons
# Input Tensor Shape: [batch_size, 1000]
# Output Tensor Shape: [batch_size, 1000]
dense2 = tf.layers.dense(inputs=dense1, units=1000, activation=tf.nn.relu)
# Add dropout operation; 0.5 probability that element will be kept
dropout = tf.layers.dropout(
inputs=dense2, rate=0.5, training=mode == learn.ModeKeys.TRAIN)
# Logits layer
# Input Tensor Shape: [batch_size, 1000]
# Output Tensor Shape: [batch_size, 4]
logits = tf.layers.dense(inputs=dropout, units=nClass)
loss = None
train_op = None
# Calculate Loss (for both TRAIN and EVAL modes)
if mode != learn.ModeKeys.INFER:
onehot_labels = tf.one_hot(indices=tf.cast(labels, tf.int32), depth=nClass)
loss = tf.losses.softmax_cross_entropy(
onehot_labels=onehot_labels, logits=logits)
# Configure the Training Op (for TRAIN mode)
if mode == learn.ModeKeys.TRAIN:
train_op = tf.contrib.layers.optimize_loss(
loss=loss,
global_step=tf.contrib.framework.get_global_step(),
learning_rate=0.001,
optimizer="SGD")
# Generate Predictions
predictions = {
"classes": tf.argmax(
input=logits, axis=1)
}
# Return a ModelFnOps object
return model_fn_lib.ModelFnOps(
mode=mode, predictions=predictions, loss=loss, train_op=train_op)
but the final accuracy is really poor (0.25). So I realized that actually the paper states that the last layer is a softmax layer. So i tried changed my logits layer to
logits = tf.layers.softmax(dropout)
but when I run it, it says
ValueError: Shapes (?, 1000) and (?, 4) are incompatible
So, what I'm missing here?
The original one was correct. The softmax activation is applied while calculating the loss with tf.losses.softmax_cross_entropy. If you want to calculate it separately you should add it after the logits calculation, but without replacing it as you did.
logits = tf.layers.dense(inputs=dropout, units=nClass)
softmax = tf.layers.softmax(logits)
Or you can combine both in one, but I wouldn't recommend it. It is better to calculate the softmax with the loss.
logits = tf.layers.dense(inputs=dropout, units=nClass, activation=tf.layers.softmax)
Your classifier is not doing better than random, so I would say that the problem lays somewhere else, maybe in the data loading and preprocessing.