import sys import time import qri from keras.callbacks import EarlyStopping, ModelCheckpoint from keras.layers.core import Activation, Dense, Dropout from keras.models import Sequential from keras.optimizers import SGD # Model name MDL_NAME = "base" # Seed random number generator np.random.seed(42) # Load QRI data datasets = qri.load_data("../datasets/qri.pkl.gz") # Split into 2D datasets train_set, valid_set, test_set = datasets # Build neural network model = Sequential() model.add(Dense(48, 100, activation="relu")) model.add(Dropout(0.5)) model.add(Dense(100, 12)) # Use stochastic gradient descent and compile model sgd = SGD(lr=0.001, momentum=0.99, decay=1e-6, nesterov=True) model.compile(loss=qri.mae_clip, optimizer=sgd) # Use early stopping and saving as callbacks
from keras.layers.core import Activation, Dense, Dropout from keras.models import Sequential from keras.optimizers import SGD import sys if len(sys.argv): first = int(sys.argv[1]) # Model name MDL_NAME = "fcn-a-3hidden-thick%s" % first # Seed random number generator np.random.seed(42) # Load QRI data datasets = qri.load_data("qri.pkl.gz") # Split into 2D datasets train_set, valid_set, test_set = datasets # Build neural network model = Sequential() model.add(Dense(48, first, activation="relu")) model.add(Dropout(0.5)) model.add(Dense(first, first, activation="relu")) model.add(Dropout(0.5)) model.add(Dense(first, first, activation="relu")) model.add(Dropout(0.5)) model.add(Dense(first, 12)) # Use stochastic gradient descent and compile model
import time import qri from keras.callbacks import EarlyStopping, ModelCheckpoint from keras.layers.core import Activation, Dense, Dropout, Flatten from keras.layers.convolutional import Convolution1D, MaxPooling1D from keras.models import Sequential from keras.optimizers import SGD # Model name MDL_NAME = "cnn" # Seed random number generator np.random.seed(42) # Load QRI data datasets = qri.load_data("../datasets/qri.pkl.gz") # Split into 3D datasets datasets = [(dataset[0][:, :, np.newaxis], dataset[1]) for dataset in datasets] train_set, valid_set, test_set = datasets # Build neural network model = Sequential() model.add(Convolution1D(1, 100, 13, activation="relu")) model.add(Dropout(0.5)) model.add(Flatten()) model.add(Dense(3600, 12)) # Use stochastic gradient descent and compile model sgd = SGD(lr=0.001, momentum=0.99, decay=1e-6, nesterov=True) model.compile(loss=qri.mae_clip, optimizer=sgd)
from keras.models import Sequential from keras.optimizers import SGD import sys if len(sys.argv): first = int(sys.argv[1]) # Model name MDL_NAME = "fcn-a-3hidden-thick%s"%first # Seed random number generator np.random.seed(42) # Load QRI data datasets = qri.load_data("qri.pkl.gz") # Split into 2D datasets train_set, valid_set, test_set = datasets # Build neural network model = Sequential() model.add(Dense(48, first,activation="relu")) model.add(Dropout(0.5)) model.add(Dense(first,first,activation="relu")) model.add(Dropout(0.5)) model.add(Dense(first, first,activation="relu")) model.add(Dropout(0.5)) model.add(Dense(first, 12)) # Use stochastic gradient descent and compile model