I am new using TensorFlow and I don't know how to classify a picture with a trained model. I have already built a train, validation and test dataset for my training and all works but I want to predict on second test dataset (called test2). I am classifying pictures of digits.
I have tried this but it doesn't work :
def train_and_predict(restore=False, test_set=None):
"""
Training of the model, posibility to restore a trained model and predict on another dataset.
"""
batch_size = 50
# Regular datasets for training
train_dataset, train_labels, test_dataset, test_labels, valid_dataset, valid_labels = load_dataset(dataset_size)
if restore:
# change the testset if restoring the trained model
test_dataset, test_labels = create_dataset(test_set)
test_dataset, test_labels = reformat(test_dataset, test_labels)
batch_size = number_predictions
graph = tf.Graph()
with graph.as_default():
# Input data.
tf_train_dataset = tf.placeholder(tf.float32, shape=(batch_size, image_size, image_size, num_channels))
tf_train_labels = tf.placeholder(tf.float32, shape=(batch_size, num_labels))
tf_valid_dataset = tf.constant(valid_dataset)
tf_test_dataset = tf.constant(test_dataset)
# Variables.
K = 32 # first convolutional layer output depth
L = 64 # second convolutional layer output depth
N = 1024 # fully connected layer
W1 = tf.Variable(tf.truncated_normal([5, 5, 1, K], stddev=0.1)) # 5x5 patch, 1 input channel
B1 = tf.Variable(tf.constant(0.1, tf.float32, [K]))
W2 = tf.Variable(tf.truncated_normal([5, 5, K, L], stddev=0.1))
B2 = tf.Variable(tf.constant(0.1, tf.float32, [L]))
W3 = tf.Variable(tf.truncated_normal([7 * 7 * L, N], stddev=0.1))
B3 = tf.Variable(tf.constant(0.1, tf.float32, [N]))
W4 = tf.Variable(tf.truncated_normal([N, 10], stddev=0.1))
B4 = tf.Variable(tf.constant(0.1, tf.float32, [10]))
# Model.
def model(data, train = True):
stride = 1
Y1 = tf.nn.relu(tf.nn.conv2d(data, W1, strides=[1, stride, stride, 1], padding='SAME') + B1)
Y1 = tf.nn.max_pool(Y1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
Y2 = tf.nn.relu(tf.nn.conv2d(Y1, W2, strides=[1, stride, stride, 1], padding='SAME') + B2)
Y2 = tf.nn.max_pool(Y2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
Y3 = tf.reshape(Y2, [-1, 7*7*64])
Y4 = tf.nn.relu(tf.matmul(Y3, W3) + B3)
if train:
# drop-out during training
Y4 = tf.nn.dropout(Y4, 0.5)
return tf.matmul(Y4, W4) + B4
# Training computation.
logits = model(tf_train_dataset)
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits, tf_train_labels))
# Optimizer.
optimizer = tf.train.AdamOptimizer(1e-4).minimize(loss)
# Predictions for the training, validation, and test data.
train_prediction = tf.nn.softmax(logits)
valid_prediction = tf.nn.softmax(model(tf_valid_dataset, False))
test_prediction = tf.nn.softmax(model(tf_test_dataset, False))
# Saver
saver = tf.train.Saver()
num_steps = 1001
with tf.Session(graph=graph) as session:
if restore:
ckpt = tf.train.get_checkpoint_state('./model/')
saver.restore(session, ckpt.model_checkpoint_path)
_, l, predictions = session.run([optimizer, loss, test_prediction])
else:
tf.global_variables_initializer().run()
for step in range(num_steps):
offset = (step * batch_size) % (train_labels.shape[0] - batch_size)
batch_data = train_dataset[offset:(offset + batch_size), :, :, :]
batch_labels = train_labels[offset:(offset + batch_size), :]
feed_dict = {tf_train_dataset : batch_data, tf_train_labels : batch_labels}
_, l, predictions = session.run([optimizer, loss, train_prediction], feed_dict=feed_dict)
if (step % 100 ==0):
saver.save(session, './model/' + 'model.ckpt', global_step=step+1)
if (step % 1000 == 0):
print('\nMinibatch loss at step %d: %f' % (step, l))
test_accuracy = accuracy(test_prediction.eval(), test_labels)
return test_accuracy , predictions
So the first time, I trained the model and test, then I want to predict on the other test set:
t,p = train_and_predict() #training
t_test2, p_test2 = train_and_predict(restore=True, test_set='./test2')
Functions load_dataset
, create_dataset
and reformat
give me datasets with shape : (nb_pictures, 28, 28, 1) and labels with shape : (nb_pictures, 10).
Thank you very much for any help
You have everything that you need there. If you just want to predict you can extract the function:
with tf.Session(graph=graph) as session:
ckpt = tf.train.get_checkpoint_state('./model/')
saver.restore(session, ckpt.model_checkpoint_path)
feed_dict = {tf_train_dataset : batch_data}
predictions = session.run([test_prediction], feed_dict)
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