I'm using the estimator API to train a CNN that classifies images of shapes.
I'm able to successfully train the CNN using custom input_fn() which trains from a tfrecord file. I'm then able to predict using model.predict(predict_input_fn). The accuracy after a few epochs is >80% and then when I use model.predict() on some test data. I get >80% also. So that seems to work fine.
I wanted to save the model and then load the model and predict using that because that's what my goal is. So basically inference. When I do this and predict on my test data I am getting abysmal results. I have stripped out all preprocessing from my input_fn() and retrained. So that I am essentially passing in raw data when I am predicting. The problem persists. I'd like to know why this is happening or if I am doing something wrong. Thank you for any insights.
I'll link the relevant code My model_fn
def model_fn(features, labels, mode, params):
x = features['image_raw']
net = tf.reshape(x, [-1, 824, 463, num_channels])
net = tf.layers.conv2d(inputs=net, name='layer_conv1',
filters=32, kernel_size=11, strides=4,
padding='same', activation=tf.nn.relu)
net = tf.layers.conv2d(inputs=net, name='layer_conv2',
filters=32, kernel_size=11, strides=4,
padding='same', activation=tf.nn.relu)
net = tf.layers.conv2d(inputs=net, name='layer_conv3',
filters=32, kernel_size=5, strides=2,
padding='same', activation=tf.nn.relu)
net = tf.layers.max_pooling2d(inputs=net, pool_size=2, strides=2,padding='SAME')
net = tf.layers.conv2d(inputs=net, name='layer_conv4',
filters=32, kernel_size=3,
padding='same', activation=tf.nn.relu)
net = tf.contrib.layers.flatten(net)
net = tf.layers.dense(inputs=net, name='layer_fc1',
units=256, activation=tf.nn.relu)
net = tf.nn.dropout(net, 0.5)
net = tf.layers.dense(inputs=net, name='layer_fc_2',
units=num_classes)
logits = net
y_pred = tf.nn.softmax(logits=logits)
y_pred_cls = tf.argmax(y_pred, axis=1)
if mode == tf.estimator.ModeKeys.PREDICT:
export_outputs = {'classes': tf.estimator.export.PredictOutput({"classes": y_pred_cls})}
spec = tf.estimator.EstimatorSpec(mode=mode,predictions=y_pred_cls,export_outputs=export_outputs)
else:
cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=labels,logits=logits)
loss = tf.reduce_mean(cross_entropy)
optimizer = tf.train.AdamOptimizer(learning_rate=0.001,beta1=0.9,beta2=0.999,epsilon=1e-8,name="Adam")
train_op = optimizer.minimize(loss=loss, global_step=tf.train.get_global_step())
metrics = {"accuracy": tf.metrics.accuracy(labels, y_pred_cls)}
# Wrap all of this in an EstimatorSpec.
spec = tf.estimator.EstimatorSpec(
mode=mode,
loss=loss,
train_op=train_op,
eval_metric_ops=metrics
)
return spec
My serving function:
def serving_input_receiver_fn():
inputs = {"image_raw": tf.placeholder(shape=[824, 463], dtype=tf.float32)}
return tf.estimator.export.ServingInputReceiver(inputs, inputs)
How I save my trained model:
export_dir = model.export_savedmodel(
export_dir_base="./saved_model/",
serving_input_receiver_fn=serving_input_receiver_fn,
as_text=True)
How I predict from the saved model:
from tensorflow.contrib import predictor
predict_fn = predictor.from_saved_model('./saved_model/1518601120/')
a = np.ones(shape=(824,463),dtype=np.float32)
image = Image.open((os.path.join(prediction_dir,subdir,file)))
image = np.array(image)
image=image.swapaxes(0,1)
a[:,:]=image[:,:,0] #The input is an RGBa PNG. only 1 channel is populated #with data from our shape.
prediction = predict_fn({"image_raw": a})
predictions.append((prediction['classes'][0]))
It turns out I was passing the predict function a tensor with heightwidth swapped. This was ok because my placeholder was the same shape. But once the tensor went into my model_fn() it was then reshaped to a size widthheight. Causing the image to be "squashed" before passing it through the model. This was causing the bad prediction results I was experiencing.