plasma-python/examples/test.py at master · PPPLDeepLearning/plasma-python · GitHub

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# import keras

from keras.models import Model # , Sequential

from keras.layers import Input

from keras.layers.core import Lambda, Reshape, Permute

# Dense, Activation, Dropout, Flatten

# from keras.layers.recurrent import LSTM, SimpleRNN

# from keras.layers.convolutional import Convolution1D

# from keras.layers.pooling import MaxPooling1D

# from keras.utils.data_utils import get_file

# from keras.layers.wrappers import TimeDistributed

# from keras.layers.merge import Concatenate

# from keras.callbacks import Callback

# from keras.optimizers import *

# from keras.regularizers import l1, l2, l1_l2

import numpy as np

x = np.array(range(8))

num_signals = len(x)

x = np.atleast_2d(x)

x = np.reshape(x, (1, num_signals))

print(x.shape)

print(x)

indices_0d = np.array([0, 1])

indices_1d = np.array([2, 3, 4, 5, 6, 7])

num_1D = 2

pre_rnn_input = Input(shape=(num_signals,))

pre_rnn_1D = Lambda(lambda x: x[:, len(indices_0d):], output_shape=(

len(indices_1d),))(pre_rnn_input)

pre_rnn_0D = Lambda(lambda x: x[:, :len(indices_0d)],

output_shape=(len(indices_0d),))(pre_rnn_input)

# slicer(x, indices_0d),

# lambda s: slicer_output_shape(s, indices_0d))(pre_rnn_input)

pre_rnn_1D = Reshape((num_1D, len(indices_1d)/num_1D))(pre_rnn_1D)

pre_rnn_1D = Permute((2, 1))(pre_rnn_1D)

# for i in range(model_conf['num_conv_layers']):

# pre_rnn_1D = Convolution1D(num_conv_filters, size_conv_filters,

# padding='valid',activation='relu') (pre_rnn_1D)

# pre_rnn_1D = MaxPooling1D(pool_size) (pre_rnn_1D)

# pre_rnn_1D = Flatten() (pre_rnn_1D)

# pre_rnn = Concatenate() ([pre_rnn_0D,pre_rnn_1D])

model = Model(inputs=pre_rnn_input, outputs=pre_rnn_1D)

# x_input = Input(batch_shape = batch_input_shape)

# x_in = TimeDistributed(pre_rnn_model) (x_input)

# if return_sequences:

# x_out = TimeDistributed(Dense(100, activation='tanh')) (x_in)

# x_out = TimeDistributed(Dense(1, activation=output_activation)) (x_in)

# else:

# x_out = Dense(1, activation=output_activation) (x_in)

model.compile(loss='mse', optimizer='sgd')

y = model.predict(x)

print(model.layers)

print(x)

print(y)

print(y.shape)

print(y[0, :, 0])

# bug with tensorflow/Keras --- ?????

plasma-python/examples/test.py at master · PPPLDeepLearning/plasma-python · GitHub

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