jupyter prediction, multiple tests
ml server
parent
8929e7182f
commit
14fc5083c6
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@ -355,7 +355,7 @@
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"source": [
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"#print(test_img)\n",
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"#test_img = os.path.join(test_dir, 'waterfall.png')\n",
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"display(Image(os.path.join(test_dir, 'unvetted/waterfall.png')))"
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"#display(Image(os.path.join(test_dir, 'unvetted/waterfall.png')))"
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]
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},
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{
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@ -529,7 +529,7 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"plotImages(sample_test_images[0:1])"
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"plotImages(sample_test_images[0:3])"
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]
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},
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{
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@ -551,6 +551,26 @@
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"])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Produces a result, quality ??? XXX\n",
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"#model = Sequential([\n",
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"# Conv2D(16, 3, padding='same', activation='relu', input_shape=(IMG_HEIGHT, IMG_WIDTH ,3)),\n",
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"# MaxPooling2D(),\n",
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"# Conv2D(32, 3, padding='same', activation='relu'),\n",
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"# MaxPooling2D(),\n",
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"# Conv2D(64, 3, padding='same', activation='relu'),\n",
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"# MaxPooling2D(),\n",
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"# Flatten(),\n",
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"# Dense(512, activation='relu'),\n",
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"# Dense(1, activation='sigmoid')\n",
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"#])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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@ -626,52 +646,27 @@
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},
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{
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"cell_type": "code",
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"execution_count": 319,
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"TRAINING info\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"print(\"TRAINING info\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 320,
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"data/train\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"print(train_dir)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 321,
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"data/train/good\n",
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"data/train/bad\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"print(train_good_dir)\n",
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"print(train_bad_dir)"
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@ -679,41 +674,25 @@
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},
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{
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"cell_type": "code",
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"execution_count": 322,
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"<tensorflow.python.keras.preprocessing.image.ImageDataGenerator object at 0x7f0864059e80>\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"print(train_image_generator)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 323,
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"<keras_preprocessing.image.directory_iterator.DirectoryIterator object at 0x7f084076d048>\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"print(train_data_gen)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 332,
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -722,24 +701,16 @@
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},
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{
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"cell_type": "code",
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"execution_count": 325,
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"<tensorflow.python.keras.callbacks.History object at 0x7f08642b4390>\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"print(history)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 334,
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -770,17 +741,9 @@
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},
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{
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"cell_type": "code",
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"execution_count": 327,
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"predict\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"# https://keras.io/models/sequential/\n",
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"print(\"predict\")"
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@ -788,17 +751,9 @@
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},
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{
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"cell_type": "code",
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"execution_count": 328,
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"4/4 [==============================] - 1s 220ms/step\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"pred=model.predict_generator(test_data_gen,\n",
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"steps=4,\n",
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@ -807,18 +762,9 @@
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},
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{
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"cell_type": "code",
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"execution_count": 335,
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"1/1 - 0s\n",
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"end predict\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"prediction = model.predict(\n",
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" x=test_data_gen,\n",
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@ -836,7 +782,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 336,
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -845,17 +791,9 @@
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},
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{
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"cell_type": "code",
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"execution_count": 337,
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[[0.9941876]]\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"# Show prediction score\n",
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"print(prediction)"
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@ -863,17 +801,9 @@
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},
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{
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"cell_type": "code",
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"execution_count": 338,
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[[False]]\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"#prediction_bool = (prediction >0.5)\n",
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"prediction_bool = (prediction == 1)\n",
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@ -882,17 +812,9 @@
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},
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{
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"cell_type": "code",
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"execution_count": 339,
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[[0]]\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"predictions = prediction_bool.astype(int)\n",
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"print(predictions)"
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@ -900,20 +822,12 @@
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},
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{
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"cell_type": "code",
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"execution_count": 345,
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Observation: bad\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"# Make final prediction\n",
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"if prediction_bool == False:\n",
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"if prediction_bool[0] == False:\n",
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" rating = 'bad'\n",
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"else:\n",
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" rating = 'good'\n",
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@ -925,14 +839,26 @@
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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"source": [
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"if prediction_bool[1] == False:\n",
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" rating = 'bad'\n",
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"else:\n",
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" rating = 'good'\n",
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"print('Observation: %s' % (rating))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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"source": [
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"if prediction_bool[2] == False:\n",
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" rating = 'bad'\n",
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"else:\n",
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" rating = 'good'\n",
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"print('Observation: %s' % (rating))"
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]
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},
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{
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"cell_type": "code",
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@ -1611,6 +1537,15 @@
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" rating = 'good'\n",
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"print('Observation: %s' % (rating))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# THE END"
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]
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}
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],
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"metadata": {
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