Я строю один слой lstm ячейку с нуля, но постоянно получаю эту ошибку:
(base) C:\Users\Rohan Baisantry\Desktop\Python, ML, Dl, RL and AI\pythonfiles\LS
TM>python lstm_scratch_1layer.py
C:\Users\Rohan Baisantry\Anaconda3\lib\site-packages\sklearn\cross_validation.py
:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of
the model_selection module into which all the refactored classes and functions a
re moved. Also note that the interface of the new CV iterators are different fro
m that of this module. This module will be removed in 0.20.
"This module will be removed in 0.20.", DeprecationWarning)
C:\Users\Rohan Baisantry\Anaconda3\lib\site-packages\h5py\__init__.py:36: Future
Warning: Conversion of the second argument of issubdtype from `float` to `np.flo
ating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(f
loat).type`.
from ._conv import register_converters as _register_converters
Object of class lstm_network initialized with the given configuration
data loaded succesfully
Traceback (most recent call last):
File "C:\Users\Rohan Baisantry\Anaconda3\lib\site-packages\tensorflow\python\f
ramework\ops.py", line 1567, in _create_c_op
c_op = c_api.TF_FinishOperation(op_desc)
tensorflow.python.framework.errors_impl.InvalidArgumentError: Shape must be rank
2 but is rank 1 for 'MatMul_1' (op: 'MatMul') with input shapes: [9], [9,9].
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "lstm_scratch_1layer.py", line 191, in <module>
main()
File "lstm_scratch_1layer.py", line 185, in main
test_object.build_graph()
File "lstm_scratch_1layer.py", line 92, in build_graph
self.ft = tf.sigmoid( tf.matmul([x[i1][i2]], self.w_fgate) + tf.matmul(self.
ht_prev, self.u_fgate) )
File "C:\Users\Rohan Baisantry\Anaconda3\lib\site-packages\tensorflow\python\o
ps\math_ops.py", line 2122, in matmul
a, b, transpose_a=transpose_a, transpose_b=transpose_b, name=name)
File "C:\Users\Rohan Baisantry\Anaconda3\lib\site-packages\tensorflow\python\o
ps\gen_math_ops.py", line 4567, in mat_mul
name=name)
File "C:\Users\Rohan Baisantry\Anaconda3\lib\site-packages\tensorflow\python\f
ramework\op_def_library.py", line 787, in _apply_op_helper
op_def=op_def)
File "C:\Users\Rohan Baisantry\Anaconda3\lib\site-packages\tensorflow\python\f
ramework\ops.py", line 3392, in create_op
op_def=op_def)
File "C:\Users\Rohan Baisantry\Anaconda3\lib\site-packages\tensorflow\python\f
ramework\ops.py", line 1734, in __init__
control_input_ops)
File "C:\Users\Rohan Baisantry\Anaconda3\lib\site-packages\tensorflow\python\f
ramework\ops.py", line 1570, in _create_c_op
raise ValueError(str(e))
ValueError: Shape must be rank 2 but is rank 1 for 'MatMul_1' (op: 'MatMul') wit
h input shapes: [9], [9,9].
(base) C:\Users\Rohan Baisantry\Desktop\Python, ML, Dl, RL and AI\pythonfiles\LS
TM>
Код здесь:
# LSTM [ Many to One ]
# START
# imports
import csv
import numpy as np
from sklearn.cross_validation import train_test_split
import tensorflow as tf
import sys
import os
import json
from random import shuffle
from tensorflow.python import debug as tf_debug
# CALCULATE ALL POSSIBLE BATCH SIZES
def calculate_batch_sizes(n_train):
batch_sizes = []
for i in range(2, int(n_train/2)):
if n_train % i == 0 and n_train / i > 1:
batch_sizes.append(i)
return batch_sizes
class lstm_network():
name = "lstm_"
# initialization function
def __init__(self, config_params):
self.sequence_length = config_params["sequence_length"]
self.batch_size = config_params["batch_size"]
self.hidden_layers_size = config_params["hidden_layers_size"]
self.data_path = config_params["data_path"]
self.n_epochs = config_params["no_of_epochs"]
self.learning_rate = config_params["learning_rate"]
self.w_igate, self.w_fgate, self.w_ogate, self.w_cgate = tf.get_variable('w_igate', shape = [self.sequence_length, self.hidden_layers_size], initializer = tf.contrib.layers.xavier_initializer()), tf.get_variable('w_fgate', shape = [self.sequence_length, self.hidden_layers_size], initializer = tf.contrib.layers.xavier_initializer()), tf.get_variable('w_ogate', shape = [self.sequence_length, self.hidden_layers_size], initializer = tf.contrib.layers.xavier_initializer()), tf.get_variable('w_cgate', shape = [self.sequence_length, self.hidden_layers_size], initializer = tf.contrib.layers.xavier_initializer())
self.u_igate, self.u_fgate, self.u_ogate, self.u_cgate = tf.get_variable('u_igate', shape = [self.hidden_layers_size, self.hidden_layers_size], initializer = tf.contrib.layers.xavier_initializer()), tf.get_variable('u_fgate', shape = [self.hidden_layers_size, self.hidden_layers_size], initializer = tf.contrib.layers.xavier_initializer()), tf.get_variable('u_ogate', shape = [self.hidden_layers_size, self.hidden_layers_size], initializer = tf.contrib.layers.xavier_initializer()), tf.get_variable('u_cgate', shape = [self.hidden_layers_size, self.hidden_layers_size], initializer = tf.contrib.layers.xavier_initializer())
self.outputs = [0.0] * self.batch_size
self.testing_loss = float(0)
self.training_loss = float(0)
self.ft, self.ct, self._ct, self.it = [0.0]*(self.hidden_layers_size), [0.0]*(self.hidden_layers_size), [0.0]*(self.hidden_layers_size), [0.0]*(self.hidden_layers_size)
self.ot, self.ht, self.ct_prev, self.ht_prev = [0.0]*(self.hidden_layers_size), [0.0]*(self.hidden_layers_size), [0.0]*(self.hidden_layers_size), [0.0]*(self.hidden_layers_size)
self.w_output_layer = tf.get_variable('w_output_layer', shape = [self.hidden_layers_size, 1], initializer = tf.contrib.layers.xavier_initializer())
print("\n Object of class lstm_network initialized with the given configuration")
# print values function
def print_model_info(self):
print("\n\n\n\t\t MODEL INFORMATION\n\n")
print("\n Weights of the LSTM layer: ")
print("\n\n input Gate Weights: \n w: ", self.w_igate,"\n u: ", self.u_igate)
print("\n\n Forget Gate Weights: \n w: ", self.w_fgate,"\n u: ", self.u_fgate)
print("\n\n Context Gate Weights: \n w: ", self.w_cgate,"\n u: ", self.u_cgate)
print("\n\n Output Gate Weights: \n w: ", self.w_ogate,"\n u: ", self.u_ogate)
print("\n\n Average loss while training: ", self.training_loss)
print("\n\n Average loss while testing: ", self.testing_loss)
# loading function
def load_data(self):
with open(self.data_path, 'r') as data_file:
data_reader = csv.reader(data_file, delimiter = ',')
self.data = [float(row[1]) for row in data_reader]
self.data_max, self.data_min, self.n_data = float(max(self.data)), float(min(self.data)), len(self.data)
for i in range(len(self.data)):
self.data[i] = float( (self.data[i]-self.data_min)/(self.data_max-self.data_min) )
self.data_x = [ self.data[i:i+self.sequence_length] for i in range(self.n_data - self.sequence_length-1)]
self.data_y = [ self.data[i] for i in range(self.sequence_length+1, self.n_data)]
self.n_data = len(self.data_x)
temp = list(zip(self.data_x,self.data_y))
shuffle(temp)
test_size = 0.25
self.data_x, self.data_y = zip(*temp)
self.trainx, self.trainy, self.testx, self.testy = self.data_x[:-int(test_size*self.n_data)], self.data_y[:-int(test_size*self.n_data)], self.data_x[-int(test_size*self.n_data):], self.data_y[-int(test_size*self.n_data):]
self.n_train, self.n_test = len(self.trainx), len(self.testx)
batch_sizes = []
batch_sizes.extend(calculate_batch_sizes(self.n_train))
while self.batch_size not in batch_sizes:
print("\n batch size provided cannot be used, please select one from the following batch sizes:\n",batch_sizes)
self.batch_size = int(input("\n enter a batch size: "))
self.n_train_batches = int( self.n_train/self.batch_size )
self.trainx, self.trainy, self.testx, self.testy = np.array(self.trainx), np.array(self.trainy), np.array(self.testx), np.array(self.testy)
self.trainx_batches, self.trainy_batches = self.trainx.reshape(self.n_train_batches, self.batch_size, self.sequence_length), self.trainy.reshape(self.n_train_batches,self.batch_size, 1)
print("\n data loaded succesfully\n")
# graph building function
def build_graph(self):
outputs = [float(0.0)]*self.batch_size
x = tf.placeholder(tf.float32, shape = [self.n_train_batches, self.batch_size, self.sequence_length])
for i1 in range(self.n_train_batches):
for i2 in range(self.batch_size):
#self.ht_prev = [self.ht_prev[i:i+9] for i in range(0, self.hidden_layers_size, 9)]
self.ft = tf.sigmoid( tf.matmul([x[i1][i2]], self.w_fgate) + tf.matmul(self.ht_prev, self.u_fgate) )
self.it = tf.sigmoid( tf.matmul([x[i1][i2]], self.w_igate) + tf.matmul(self.ht_prev, self.u_igate) )
self.ot = tf.sigmoid( tf.matmul([x[i1][i2]], self.w_ogate) + tf.matmul(self.ht_prev, self.u_ogate) )
self._ct = tf.sigmoid( tf.matmul([x[i1][i2]], self.w_cgate) + tf.matmul(self.ht_prev, self.u_cgate) )
self.ct = tf.tanh(tf.multiply(self.ft, self.ct_prev) + tf.multiply(self.it, self._ct))
self.ht = tf.multiply(self.ot, self.ct)
self.ht_prev = self.ht
self.ct_prev = self.ct
outputs[i2] = tf.nn.relu( tf.matmul(self.ht, self.w_output_layer) )[0]
self.loss = tf.reduce_mean(tf.square(outputs - self.trainy_batches[i1]))
self.train_op = tf.train.AdamOptimizer(learning_rate=self.learning_rate).minimize(loss)
print("\n Graph built")
# training function
def train(self):
i = 0
avg_loss = float(0)
with tf.session() as sess:
sess.run(tf.global_variables_initializer())
sess = tf_debug.LocalCLIDebugWrapperSession(sess)
for ep in self.n_epochs:
sess.run(self.train_op, feed_dict= { x: self.trainx_batches})
if ep % 10 == 0:
i += 1
mse = loss.eval()
avg_loss = float(avg_loss + mse)
print("\n Epoch: ", ep, "\t Loss: ", mse)
avg_loss = float(avg_loss/i)
self.training_loss = avg_loss
print("\n Training Loss: ", avg_loss)
# Predict function
def predict(self):
x_test_row = tf.placeholder(tf.float32, shape = [1, self.sequence_length])
avg_loss = float(0)
input_row = []
output_row = [0.0]
predictions = []
# one forward pass
self.ft = tf.sigmoid( tf.matmul([x_test_row], self.w_fgate) + tf.matmul([self.ht_prev], self.u_fgate) )
self.it = tf.sigmoid( tf.matmul([x_test_row], self.w_igate) + tf.matmul([self.htprev], self.u_igate ) )
self.ot = tf.sigmoid( tf.matmul([x_test_row], self.w_ogate) + tf.matmul([self.ht_prev], self.u_ogate) )
self._ct = tf.sigmoid( tf.matmul([x_test_row], self.w_cgate) + tf.matmul([self.ht_prev], self.u_cgate) )
self.ct = yf.tanh(tf.multiply(self.ft, self.ct_prev) + tf.multiply(self.it, self._ct))
self.ht = tf.matmul(self.ot,self.ct)
pred_output = tf.nn.relu( tf.matmul(self.ht, self.w_output_layer) )
with tf.session() as sess:
for i1 in range(self.n_test):
del input_row[:]
output_row[0] = float(self.testy[i1])
for i2 in range(self.sequence_length):
input_row.append(self.testx[i1][i2])
sess.run(pred_output, feed_dict = { x_test_row: np.array(input_row).reshape(1, self.sequence_length)})
predictions.append(pred_output)
avg_error += abs(pred_output - output_row)
avg_error = float( avg_error/i1 )
self.testing_loss = avg_error
print("\n testing Error: ", avg_error)
return np.array(predictions)
# save model function
def save_model(self):
f = open("model_info.txt", "w+")
f.write("\n\n\n\n\t\t MODEL INFORMATION\n\n")
f.write("\n\n Weights of the LSTM layer: ")
f.write("\n\n\n input Gate Weights: \n w: ", self.w_igate,"\n u: ", self.u_igate)
f.write("\n\n\n Forget Gate Weights: \n w: ", self.w_fgate,"\n u: ", self.u_fgate)
f.write("\n\n\n Context Gate Weights: \n w: ", self.w_cgate,"\n u: ", self.u_cgate)
f.write("\n\n\n Output Gate Weights: \n w: ", self.w_ogate,"\n u: ", self.u_ogate)
f.write("\n\n\n Average loss while training: ", self.training_loss)
f.write("\n\n\n Average loss while testing: ", self.testing_loss)
f.close()
print("\n\n model's information saved in model_info.txt and weights stored in model.json\n\n")
f = open("model.json", "w+")
model_dict = { 'w_output_layer': self.w_output_layer, 'w_igate': self.w_igate, 'u_igate': self.u_igate, 'w_fgate': self.w_fgate, 'u_fgate': self.u_fgate, 'w_cgate': self.w_cgate, 'u_cgate': self.u_cgate, 'w_ogate': self.w_ogate, 'u_ogate': self.u_gate }
f.write(str(model_dict))
f.close()
# main function()
def main():
# parameters of the network
config_params = dict()
config_params["sequence_length"] = 3
config_params["batch_size"] = 33
config_params["hidden_layers_size"] = 9
config_params["data_path"] = "C:\\Users\\Rohan Baisantry\\Desktop\\Python, ML, Dl, RL and AI\\pythonfiles\\LSTM\\data.csv"
config_params["no_of_epochs"] = 1500
config_params["learning_rate"] = 0.01
# object of class lstm_network
test_object = lstm_network(config_params)
test_object.load_data()
test_object.build_graph()
test_object.train()
predictions = test_object.predict()
test_object.save_model()
# run
main()
Примечание:
В месте ошибки я заменил self.ht_prev на [self.ht_prev], и он выдал ошибку, что ht_prev = [1,1,9] vs self.u_gate = [9,9]
Итак, я изменил ее на [self.prev][0], и она дает исходную ошибку