I,ve followed the tutorial in tensorflow tutorial to build a MNIST model for hand written digits recognition. I want to test the model by inputing a single image to the classifier and get the output it predicted.
Here is the complete code of the classifier.
I've tried by reading the image using imread but it didn't worked
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import tensorflow as tf
import pylab
import os
from scipy.misc import imread
import matplotlib.image as mpimg
import matplotlib.pyplot as plt
tf.logging.set_verbosity(tf.logging.INFO)
def cnn_model_fn(features, labels, mode):
input_layer = tf.reshape(features["x"],[-1,28,28,1])
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,
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=10)
predictions = {
"classes":tf.argmax(input=logits, axis=1),
"probabilites": tf.nn.softmax(logits, name="softmax_tensor")
}
if mode == tf.estimator.ModeKeys.PREDICT:
return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions)
loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits)
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)
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):
# Load training and eval data
mnist = tf.contrib.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 = 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
train_input_fn = tf.estimator.inputs.numpy_input_fn(
x={"x": train_data},
y=train_labels,
batch_size=100,
num_epochs=None,
shuffle=True)
mnist_classifier.train(
input_fn=train_input_fn,
steps=20000,
hooks=[logging_hook])
# Evaluate the model and print results
eval_input_fn = tf.estimator.inputs.numpy_input_fn(
x={"x": eval_data},
y=eval_labels,
num_epochs=1,
shuffle=False)
eval_results = mnist_classifier.evaluate(input_fn=eval_input_fn)
print(eval_results)
