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satnogs-wut
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notes, debug

master
ml server 2020-01-04 18:36:35 -07:00
parent 83fbb7d6d9
commit 6cef79c5ac
1 changed files with 60 additions and 2 deletions
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@ -26,7 +26,22 @@ from tensorflow.python.keras.models import load_model
from tensorflow.python.keras.preprocessing.image import load_img
from tensorflow.python.keras.preprocessing.image import img_to_array
# XXX
from tensorflow.python.keras.models import Model
from tensorflow.python.keras.layers import Input, concatenate
#from tensorflow.python.keras.optimizers import Adam
# XXX Plot
from tensorflow.python.keras.utils import plot_model
from tensorflow.python.keras.callbacks import ModelCheckpoint
## for visualizing
import matplotlib.pyplot as plt, numpy as np
from sklearn.decomposition import PCA
# https://keras.io/preprocessing/image/
# TODO:
# * Pre-process image
print("datagen")
datagen = ImageDataGenerator(
featurewise_center=False,
@ -56,9 +71,19 @@ print("datagen.flow")
train_it = datagen.flow_from_directory('data/train/', class_mode='binary')
val_it = datagen.flow_from_directory('data/val/', class_mode='binary')
test_it = datagen.flow_from_directory('data/test/', class_mode='binary')
print("train_it.next()")
batchX, batchy = train_it.next()
print('Batch shape=%s, min=%.3f, max=%.3f' % (batchX.shape, batchX.min(), batchX.max()))
trainX, trainY = train_it.next()
print('Batch shape=%s, min=%.3f, max=%.3f' % (trainX.shape, trainX.min(), trainX.max()))
valX, valY = val_it.next()
print('Batch shape=%s, min=%.3f, max=%.3f' % (valX.shape, valX.min(), valX.max()))
testX, testY = test_it.next()
print('Batch shape=%s, min=%.3f, max=%.3f' % (testX.shape, testX.min(), testX.max()))
print("input shape")
input_shape=trainX.shape[1:]
print(input_shape)
#img_width=823
#img_height=1606
img_width=256
@ -71,26 +96,51 @@ print("Sequential")
model = Sequential()
print("add")
# Other data to consider adding:
# * JSON metadata
# * TLE
# * Audio File (ogg)
# * Decoded Data (HEX, ASCII, PNG)
# Data from external sources to consider adding:
# * Weather
print("convolution 2 deeeee")
# https://keras.io/layers/convolutional/
#model.add(Convolution2D(32, 3, 3, input_shape=trainX.shape[1:]))
#model.add(Convolution2D(32, 3, 3, input_shape=(255,255,3)))
model.add(Convolution2D(32, 3, 3, input_shape=(img_width, img_height,3)))
# https://keras.io/activations/
print("Activation relu")
model.add(Activation('relu'))
# https://keras.io/layers/pooling/
print("Pooling")
model.add(MaxPooling2D(pool_size=(2, 2)))
print("Convolution2D")
model.add(Convolution2D(32, 3, 3))
print("Activation relu")
model.add(Activation('relu'))
print("Pooling")
model.add(MaxPooling2D(pool_size=(2, 2)))
print("Convolution2D")
model.add(Convolution2D(64, 3, 3))
print("Activation relu")
model.add(Activation('relu'))
print("Pooling")
model.add(MaxPooling2D(pool_size=(2, 2)))
# https://keras.io/layers/core/
print("Flatten")
model.add(Flatten())
# https://keras.io/layers/core/
print("Dense")
model.add(Dense(64))
print("Activation relu")
model.add(Activation('relu'))
# https://keras.io/layers/core/
print("Dropout")
model.add(Dropout(0.5))
print("Dense")
model.add(Dense(1))
print("Activation softmax")
model.add(Activation('softmax'))
# https://keras.io/models/sequential/
@ -104,6 +154,8 @@ model.compile(
optimizer='rmsprop',
metrics=['accuracy'])
# https://keras.io/models/sequential/
print("fit")
model.fit(
@ -129,6 +181,12 @@ model.fit(
# https://keras.io/models/sequential/
# evaluate(x=None, y=None, batch_size=None, verbose=1, sample_weight=None, steps=None, callbacks=None, max_queue_size=10, workers=1, use_multiprocessing=False)
# TODO:
# * Generate output to visualize training/validating/testing.
# Plot, fail
#print("plot")
#plot_model(test_it, to_file='data/wut-plot.png', show_shapes=True, show_layer_names=True)
# https://keras.io/models/sequential/
print("predict")
prediction = model.predict(