Related
TF 2.x - just for the experience I tried with a simple experimental dataset - to show the problem:
import numpy as np
import tensorflow as tf
import keras
from tensorflow.keras.callbacks import LambdaCallback
import tensorflow_datasets as tfds
data, info = tfds.load('iris', split='train[:80%]',
as_supervised=True, with_info=True)
print(info)
features, labels = tuple(zip(*data))
# NB: the generator should yield a dictionary for the inputs, and the output as is.
def gen(x_train, y_train):
print('generator initiated')
(x_train, y_train)= tfds.load('iris', shuffle_files=True, as_supervised=True, with_info=True)
idx = 0
while True:
yield tf.transpose([x_train[:32], tf.one_hot(y_train[:32])])
print('generator yielded a batch %d' % idx)
idx += 1
train_ds = tf.data.Dataset.from_generator(gen, args=(features, labels),
output_types=(tf.float32, tf.int32),
output_shapes=(tf.TensorShape([32,4]), tf.TensorShape([32,4 ])),
)
# OR
#output_signature=(
# tf.TensorSpec(shape=(4,), dtype=tf.float32),
# tf.TensorSpec(shape=(), dtype=tf.int32)),
#)
# datasetGen = iter(train_ds)
model = tf.keras.models.Sequential()
model.add(tf.keras.layers.Dense(512, activation='relu', input_shape=(32,4,))) # 4 fields
model.add(tf.keras.layers.Dense(4, activation='softmax'))
model.compile(optimizer='rmsprop',
loss='categorical_crossentropy',
metrics=['accuracy'])
train_ds= train_ds.batch(32).prefetch(32)
# callbacks=[LambdaCallback(on_epoch_end=generator.on_epoch_end)],
history= model.fit(train_ds, epochs = 7, verbose = 1)
print(history.history['accuracy'])
& am getting :
In ln: yield tf.transpose([x_train[:32], tf.one_hot(y_train[:32])])
TypeError: unhashable type: 'slice'
problem seems to be here - x_train[:32] ?
Q ?? how to make corrections to the code (either to the generator-func? or to the output_signature? or to the input_shape=? or somewhere else) to be able to use Dataset in model.fit() method ?
(sorry for dummy example, but I'd like to test generator-func use in model.fit())
well, it was really a dummy example of generator use; & moreover tf.data always win in speed compared with generator use. Nevertheless, such works (code also needs refactoring - e.g. or organizing pipelines for BigData - e.g.)
import tensorflow as tf
import numpy as np
import pandas as pd
# LOAD DATA
df= pd.read_csv('https://gist.githubusercontent.com/netj/8836201/raw/6f9306ad21398ea43cba4f7d537619d0e07d5ae3/iris.csv', dtype = 'float32', converters = {'variety' : str},
nrows=64, decimal='.')
# df.head()
_features=df.iloc[:,:4].copy()
_labels=df.iloc[:,-1:].copy()
_labels['variety1'] = pd.factorize(_labels['variety'])[0]
_target= _labels['variety1'].astype(np.int64).copy()
_targets= _target[:,np.newaxis]
#print(_features)
print(type(_targets))
# SPLIT for Train & Test
# https://www.kdnuggets.com/2020/07/getting-started-tensorflow2.html
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(_features,_targets, test_size=0.3)
# Typically, we normalize the data when we have a high amount of variance in it.
print(X_train.var())
print(X_test.var())
# Here we can see that both X_train and X_test have very low variance, so no need to normalize the data.
# PREPROCESSING
#
# to_categorical
y_train = tf.keras.utils.to_categorical(y_train)
y_test = tf.keras.utils.to_categorical(y_test)
print(y_train[:5,:])
# convert our data to numpy arrays
X_train = X_train.values
X_test = X_test.values
#################################################
#################################################
def gen(_features, _labels):
x_train= _features
y_train= _labels
#print('gen:\n', list(x_train))
#print('gen:\n', list(y_train))
idx = 0
while idx<64:
yield x_train[:32], y_train[:32]
print('generator yielded a batch %d' % idx)
idx += 1
#################################################
# train_ds <<<<<<<<<<<<<<<<<<<<<<<
train_ds = tf.data.Dataset.from_generator(gen, args=(X_train, y_train),
output_types=(tf.float32, tf.int64),
output_shapes=(tf.TensorShape([32,4]), tf.TensorShape([32, 2 ])),
)
# OR
#output_signature=(
# tf.TensorSpec(shape=(4,), dtype=tf.float32),
# tf.TensorSpec(shape=(), dtype=tf.int32)),
#)
# datasetGen = iter(train_ds)
# print('train_ds:\n',list(train_ds.as_numpy_iterator()))
#################################################
# Model
model = tf.keras.models.Sequential()
model.add(tf.keras.layers.Dense((512), activation='relu', input_shape=(32,4 ))) # 4 fields
model.add(tf.keras.layers.Dense((2), activation='softmax'))
# INSTEAD OF ONE-HOT CAN USE sparse_categorical_crossentropy HERE
model.compile(optimizer='rmsprop',
loss='categorical_crossentropy',
metrics=['accuracy'])
train_ds= train_ds.batch(32).prefetch(32)
# callbacks=[LambdaCallback(on_epoch_end=generator.on_epoch_end)],
history= model.fit(train_ds, epochs = 7, verbose = 1)
validation_ ds from source X_test, y_test formed with tf.data.Dataset.from_tensor_slices() have problems with shape (4,) instead of model's input shape (32,4,) - but it is of the inappropriate generator's task at all from the very beginningg, I think... though with train_ds evaluate() & predict() methods works (though that is not the task of ML)
##############################################
score = model.evaluate(train_ds, batch_size=32, verbose=1) # test_ds needed
print("Test Accuracy:", score[1])
y_pred = model.predict(train_ds)
print('PREDICTIONS:\n', y_pred)
##############################################
#https://medium.com/#nutanbhogendrasharma/tensorflow-deep-learning-model-with-iris-dataset-8ec344c49f91
#Print actual and predicted value
features, labels = tuple(zip(*train_ds)) # If you need the numpy array version, convert them using np.array(): # https://stackoverflow.com/a/65499385/15893581
actual = np.argmax(labels,axis=-1)
predicted = np.argmax(y_pred,axis=-1)
print(f"Actual: {actual}")
print(f"Predicted: {predicted}")
So, incoming test_ds e.g. still needs to be adopted (though better to adopt gen_func here, I think), but overall idea of using generator in TF 2.x is clear now (only if will be used for huge data)...
P.S.
and advice to improve the model here
I apologize for this dummy question, as I'm still a novice in ML, but needed to connect somehow generator & training for the experience
Finally I generated iris_dataset from function (really, not quick operation)... some attention stll needed else to repeat-fn, but code-design in general works (for really random data)
# Importing the tensorflow library
import tensorflow as tf
import numpy as np
import keras
#FeaturesDict({
# 'features': Tensor(shape=(4,), dtype=tf.float32),
# 'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=3),
#})
BATCH_SIZE= 12
EPOCHS = 7
QTY_BATCHES= 10 # to be generated
# The Dataset.from_generator constructor converts the python generator to a fully functional tf.data.Dataset.
def gen():
for i in range(BATCH_SIZE):
# should yield a pair Features - Label
data= np.expand_dims(np.random.sample(4) , axis=0)
label= [np.random.randint(3)]
yield data, label
train_ds = tf.data.Dataset.from_generator(gen,
(tf.float32, tf.int32),
(tf.TensorShape([None,4]),
tf.TensorShape([ 1])))
# Applying the Dataset.repeat() transformation with no arguments will repeat the input indefinitely.
# The Dataset.repeat transformation concatenates its arguments without signaling the end of one epoch and the beginning of the next epoch. Because of this a Dataset.batch applied after Dataset.repeat will yield batches that straddle epoch boundaries:
train_ds= train_ds.repeat(count= EPOCHS*BATCH_SIZE*QTY_BATCHES).batch(BATCH_SIZE, drop_remainder=True).prefetch(BATCH_SIZE)
NUM_CLASSES= 3
train_ds = train_ds.map(lambda x, y: (x, tf.one_hot(y, depth=NUM_CLASSES)))
for x, y in train_ds:
print(x)
print(y)
# Build a simple linear model
model = tf.keras.models.Sequential()
model.add(tf.keras.layers.Dense(64, activation='relu', input_shape=(None,4))) # unknown(variable) batch_size, 4 fields
model.add(tf.keras.layers.Dense(3, activation='softmax'))
model.compile(optimizer='rmsprop',
loss='categorical_crossentropy',
metrics=['accuracy'])
# steps_per_epoch = int( np.ceil(x_train.shape[0] / batch_size) )
# The Steps per epoch denote the number of batches to be selected for one epoch. If 500 steps are selected then the network will train for 500 batches to complete one epoch.
history= model.fit(train_ds, batch_size=BATCH_SIZE, epochs= EPOCHS, \
steps_per_epoch= (QTY_BATCHES*BATCH_SIZE)//BATCH_SIZE, \
verbose = 1)
print(history.history['accuracy'])
print(history.history['loss'])
# Keras - Plot training, validation and test set accuracy
# https://stackoverflow.com/questions/41908379/keras-plot-training-validation-and-test-set-accuracy
import keras
from matplotlib import pyplot as plt
plt.plot(history.history['accuracy'])
#plt.plot(history.history['val_acc'])
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
#plt.legend(['train', 'val'], loc='upper left')
plt.legend(['train'], loc='upper left')
plt.show()
plt.plot(history.history['loss'])
# plt.plot(history.history['val_loss'])
plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epoch')
# plt.legend(['train', 'val'], loc='upper left')
plt.legend(['train'], loc='upper left')
plt.show()
ok, I'v got working case for the initial Dataset:
import numpy as np
import tensorflow as tf
import keras
from tensorflow.keras.callbacks import LambdaCallback
import tensorflow_datasets as tfds
data, info = tfds.load('iris', split='train[:100%]', batch_size=10, as_supervised=True, with_info=True)
print(info)
NUM_CLASSES= info.features["label"].num_classes
data = data.map(lambda x, y: (x, tf.one_hot(y, depth=NUM_CLASSES)))
features, labels = tuple(zip(*data))
print(features)
print(labels)
# NB: the generator should yield a dictionary for the inputs, and the output as is.
def gen(x_train, y_train):
print('generator initiated')
print(x_train.shape)
print(y_train.shape)
idx = 0
while True:
yield x_train, y_train
print('generator yielded a batch %d' % idx)
idx += 1
train_ds = tf.data.Dataset.from_generator(gen, args=(features, labels),
output_types=(tf.float32, tf.int32),
output_shapes=(tf.TensorShape([None,10,4]), tf.TensorShape([ None, 10, 3 ])),
)
# OR (better! because prev. is Deprecated)
#output_signature=(
# tf.TensorSpec(shape=(4,), dtype=tf.float32),
# tf.TensorSpec(shape=(), dtype=tf.int32)),
#)
#it = iter(train_ds)
#print(it.get_next())
for feature, label in train_ds:
print("shape of ds_generated: ", feature.shape,label.shape)
break
#num_val = len(train_ds) # TypeError: The dataset length is unknown. BECAUSE it is FLOW
#print(num_val)
model = tf.keras.models.Sequential()
model.add(tf.keras.layers.Dense(512, activation='relu', input_shape=(None,10,4))) # 4 fields
model.add(tf.keras.layers.Dense(3, activation='softmax'))
model.compile(optimizer='rmsprop',
loss='categorical_crossentropy',
metrics=['accuracy'])
train_ds= train_ds.batch(32).prefetch(32)
# callbacks=[LambdaCallback(on_epoch_end=generator.on_epoch_end)],
history= model.fit(train_ds, epochs = 2, steps_per_epoch= 120 // 10, verbose = 1)
print(history.history['accuracy'])
one-hot encoding I've moved out of gen_func-scope
divided DS for features & labels
! gave correct input_shape to model (& appropriate shape changes in gen_func) - according [variable_rows_count_in_batch, batch_size, columns_features]
verbose = 1 for readable Debug in MT env.
advice from here
to define a variable batch size with None and setting the
steps_per_epoch
-- still not helps if taking split='train[:50%]' and steps_per_epoch= 60 // 10, -- as for unfully filled LAST batch -- the source of problem in my code IS in gen_func output_shapes -- that is clear here, because gen_func really was got dummy for testing purposes...
for real cases use Logical Output ! and appropriate Shapes
P.S.
though for 5 epochs I am getting:
Graph execution error: >> ZMQError: Too many open file
AttributeError: '_thread._local' object has no attribute 'event_pipe'
-- ! probably, NOT enough memory to finish training !... - decreasing output in Dense(512,..) HELPS (as well as decreasing number of epochs)
I have a question about the input and output data in a RNN or LSTM. A RNN expects a 3-dimensional vector as input of the form (Batch_size, sequence_length_input, features_input) and a 3-dimensional output vector of the form (Batch_size, sequence_length_output, features_output).
I know that the features_input and features_output don't have to have the same number while the Batch_size has to be equal for input and output. But what about the middle part sequence_length_input and sequence_length_output. Do they have to be the same? At least in my example (with Keras and Tensorflow) I always get an error if they are not the same. So I am wondering whetever I have a bug in the code or if this is generally not possible.
So can I for example use as input for the training, the data X_train =(1000, 100, 10) and the output Y_train = (1000, 20, 3) such that I have a mapping for each of the 1000 itmes (Batch_size) from a 10-dimensional (features_input) time series with 100 time steps (sequence_length_input) to a 3-dimensional (features_output) time series with 20 time steps (sequence_length_output).
Update: Here is my code with a RNN for time series forecasting that only works if the sequence_length of the input steps_backward is equal to the sequence_length of the output steps_forward otherwise it will throw a ValueError:
ValueError: Dimensions must be equal, but are 192 and 96 for '{{node mean_squared_error/SquaredDifference}} = SquaredDifference[T=DT_FLOAT](sequential_5/time_distributed_5/Reshape_1, IteratorGetNext:1)' with input shapes: [?,192,1], [?,96,1].
In the code I use the 96 past timesteps (or 2*96=192 timesteps) to predict the future 96 timesteps. When the number of past and future timesteps are equal (equal sequence_length), everything works fine. Otherwise (unequal sequence_length) I get the ValueError.
Code:
#Import modules
import pandas as pd
import numpy as np
import tensorflow as tf
from sklearn.preprocessing import StandardScaler
from tensorflow import keras
# Define the parameters of the RNN and the training
epochs = 1
batch_size = 50
steps_backwards = 2 * 96
steps_forward = 96
split_fraction_trainingData = 0.70
split_fraction_validatinData = 0.90
randomSeedNumber = 50
#Read dataset
df = pd.read_csv('C:/Users/User1/Desktop/TestData.csv', sep=';', header=0, low_memory=False, infer_datetime_format=True, parse_dates={'datetime':[0]}, index_col=['datetime'])
# standardize data
data = df.values
indexWithYLabelsInData = 0
data_X = data[:, 0:3]
data_Y = data[:, indexWithYLabelsInData].reshape(-1, 1)
scaler_standardized_X = StandardScaler()
data_X = scaler_standardized_X.fit_transform(data_X)
data_X = pd.DataFrame(data_X)
scaler_standardized_Y = StandardScaler()
data_Y = scaler_standardized_Y.fit_transform(data_Y)
data_Y = pd.DataFrame(data_Y)
# Prepare the input data for the RNN
series_reshaped_X = np.array([data_X[i:i + (steps_backwards+steps_forward)].copy() for i in range(len(data) - (steps_backwards+steps_forward))])
series_reshaped_Y = np.array([data_Y[i:i + (steps_backwards+steps_forward)].copy() for i in range(len(data) - (steps_backwards+steps_forward))])
timeslot_x_train_end = int(len(series_reshaped_X)* split_fraction_trainingData)
timeslot_x_valid_end = int(len(series_reshaped_X)* split_fraction_validatinData)
X_train = series_reshaped_X[:timeslot_x_train_end, :steps_backwards]
X_valid = series_reshaped_X[timeslot_x_train_end:timeslot_x_valid_end, :steps_backwards]
X_test = series_reshaped_X[timeslot_x_valid_end:, :steps_backwards]
Y_train = series_reshaped_Y[:timeslot_x_train_end, steps_backwards:]
Y_valid = series_reshaped_Y[timeslot_x_train_end:timeslot_x_valid_end, steps_backwards:]
Y_test = series_reshaped_Y[timeslot_x_valid_end:, steps_backwards:]
# Build the model and train it
np.random.seed(randomSeedNumber)
tf.random.set_seed(randomSeedNumber)
model = keras.models.Sequential([
keras.layers.SimpleRNN(10, return_sequences=True, input_shape=[None, 3]),
keras.layers.SimpleRNN(10, return_sequences=True),
keras.layers.TimeDistributed(keras.layers.Dense(1))
])
model.compile(loss="mean_squared_error", optimizer="adam")
history = model.fit(X_train, Y_train, epochs=epochs, batch_size=batch_size, validation_data=(X_valid, Y_valid))
#Predict the test data
Y_pred = model.predict(X_test)
and here is some test data https://filetransfer.io/data-package/ufbzh09o#link
Reminder: The code and the data provide a Minimal reproducible example. Maybe you can have a look at it as in this code the sequence_length has to be equal for the input and output data, otherwise I get an error. Unfortuantely I still have not figured out why this this problem occurs
I have encountered the same problems. My input data shape is [512,10,3], and the output data is [512,20,1], which means that the last ten-time time steps data is used to predict the future twenty-time time steps. When I tried to implement it in PyTorch, the same problem as you appeared. Finally, I just used the last state of the LSTM to repeat 20 times and feed into the next fully connected layers. However, I cannot do it in the classic backpropagation (just made up of fully connected layers) neural network.
I am running a simple encoder-decoder setup to train a representation for a one dimensional image. In this sample the input are lines with varying slopes and in the encoded layer we would expect something that resembles the slope. My setup is keras with a tensorflow backend. I am very new to this as well.
It all works fine, at least until I move away from steps_per_epoch to batch_size in the model.fit() method. Certain values of the batch_size, such as 1,2,3, 8 and 16 do work, for others I get a value error. My initial guess was 2^n, but that did not work.
The error I get for batch_size = 5
ValueError: operands could not be broadcast together with shapes (5,50) (3,50) (5,50)
I am trying to understand which relation between batch_size and training data is valid such that it always passes. I assumed that the training set would be simply divided into floor(N/batch_size) batches and the remainder would be processed as such.
My questions are:
What is the relation between size of data set and batch_size that are allowed.
What exactly is the keras/tensorflow trying to do such that the batch_size is important?
Thank you very much for the help.
The code to reproduce this is
import numpy as np
from keras.models import Model
from keras.layers import Input, Dense, Conv1D, Concatenate
from keras.losses import mse
from keras.optimizers import Adam
INPUT_DIM = 50
INTER_DIM = 15
LATENT_DIM = 1
# Prepare Sample Data
one_line = np.linspace(1, 30, INPUT_DIM).reshape(1, INPUT_DIM)
test_array = np.repeat(one_line, 1000, axis=0)
slopes = np.linspace(0, 1, 1000).reshape(1000, 1)
data = test_array * slopes
# Train test split
train_mask = np.where(np.random.sample(1000) < 0.8, 1, 0).astype('bool')
x_train = data[train_mask].reshape(-1, INPUT_DIM, 1)
x_test = data[~train_mask].reshape(-1, INPUT_DIM, 1)
# Define Model
input = Input(shape=(INPUT_DIM, 1), name='input')
conv_layer_small = Conv1D(filters=1, kernel_size=[3], padding='same')(input)
conv_layer_medium = Conv1D(filters=1, kernel_size=[5], padding='same')(input)
merged_convs = Concatenate()(
[conv_layer_small, conv_layer_medium])
latent = Dense(LATENT_DIM, name='latent_layer',
activation='relu')(merged_convs)
encoder = Model(input, latent)
decoder_int = Dense(INTER_DIM, name='dec_int_layer', activation='relu')(latent)
output = Dense(INPUT_DIM, name='output', activation='linear')(decoder_int)
encoder_decoder = Model(input, output, name='encoder_decoder')
# Add Loss
reconstruction_loss = mse(input, output)
encoder_decoder.add_loss(reconstruction_loss)
encoder_decoder.compile(optimizer='adam')
if __name__ == '__main__':
epochs = 100
encoder_decoder.fit(
x_train,
epochs=epochs,
batch_size=4,
verbose=2
)
I am learning TensorFlow and am trying to train a BoostedTreesClassifier (premade estimator). However, I cannot get it to work with my bucketized columns. Below is my bucketized column:
age_bucket_column = tf.feature_column.bucketized_column(tf.numeric_column(key='age'), [20, 40, 60])
Here is my train input function (note features is a Pandas DataFrame):
def train_input_fn(features, labels, batch_size):
dataset = tf.data.Dataset.from_tensor_slices((dict(features), labels))
dataset = dataset.shuffle(buffer_size=1000).repeat(count=None).batch(batch_size)
return dataset.make_one_shot_iterator().get_next()
Here is my estimator:
boosted_trees_classifier = tf.estimator.BoostedTreesClassifier(
feature_columns=[age_bucket_column],
n_batches_per_layer=100
)
And here is my code to train it:
classifier.train(
input_fn=lambda: train_input_fn(train_X, train_y, 100),
steps=1000
)
However, when I run it, I get the following error:
ValueError: Tensor conversion requested dtype float32 for Tensor with dtype int64: 'Tensor("IteratorGetNext:13", shape=(?,), dtype=int64, device=/device:CPU:0)'
Note that when I run the same code but with another model (say a LinearClassifier or DNNClassifier) it works perfectly. What am I doing wrong? Thank you in advance!
This is probably because of your labels are of type int64. Cast them to float32
train_y = pd.Series(train_y , index=np.array(range(1, train_y.shape[0] + 1)), dtype=np.float32)
I'll preface this by saying this is my first posted question on SO. I've just recently started working with Tensorflow, and have been attempting to apply a convolutional-neural network model approach for classification of .csv records in a file representing images from scans of microarray data. (FYI: Microarrays are a grid of spotted DNA on a glass slide, representing specific DNA target sequences for determining the presence of those DNA targets in a sample. The individual pixels represent fluorescence intensity value from 0-1). The file has ~200,000 records in total. Each record (image) has 10816 pixels that represent DNA sequences from known viruses, and one index label which identifies the virus species. The pixels create a pattern which is unique to each of the different viruses. There are 2165 different viruses in total represented within the 200,000 records. I have trained the network on images of labeled microarray datasets, but when I try to pass a new dataset through to classify it/them as one of the 2165 different viruses and determine predicted values and probabilities, I don't seem to be having much luck. This is the code that I am currently using for this:
import tensorflow as tf
import numpy as np
import csv
def extract_data(filename):
print("extracting data...")
NUM_LABELS = 2165
NUM_FEATURES = 10816
labels = []
fvecs = []
rowCount = 0
#iterate over the rows, split the label from the features
#convert the labels to integers and features to floats
for line in open(filename):
rowCount = rowCount + 1
row = line.split(',')
labels.append(row[3])#(int(row[7])) #<<<IT ALWAYS PREDICTS THIS VALUE!
for x in row [4:10820]:
fvecs.append(float(x))
#convert the array of float arrasy into a numpy float matrix
fvecs_np = np.matrix(fvecs).astype(np.float32)
#convert the array of int lables inta a numpy array
labels_np = np.array(labels).astype(dtype=np.uint8)
#convert the int numpy array into a one-hot matrix
labels_onehot = (np.arange(NUM_LABELS) == labels_np[:, None]).astype(np.float32)
print("arrays converted")
return fvecs_np, labels_onehot
def TestModels():
fvecs_np, labels_onehot = extract_data("MicroarrayTestData.csv")
print('RESTORING NN MODEL')
weights = {}
biases = {}
sess=tf.Session()
init = tf.global_variables_initializer()
#Load meta graph and restore weights
ModelID = "MicroarrayCNN_Data-1000.meta"
print("RESTORING:::", ModelID)
saver = tf.train.import_meta_graph(ModelID)
saver.restore(sess,tf.train.latest_checkpoint('./'))
graph = tf.get_default_graph()
x = graph.get_tensor_by_name("x:0")
y = graph.get_tensor_by_name("y:0")
keep_prob = tf.placeholder(tf.float32)
y_ = tf.placeholder("float", shape=[None, 2165])
wc1 = graph.get_tensor_by_name("wc1:0")
wc2 = graph.get_tensor_by_name("wc2:0")
wd1 = graph.get_tensor_by_name("wd1:0")
Wout = graph.get_tensor_by_name("Wout:0")
bc1 = graph.get_tensor_by_name("bc1:0")
bc2 = graph.get_tensor_by_name("bc2:0")
bd1 = graph.get_tensor_by_name("bd1:0")
Bout = graph.get_tensor_by_name("Bout:0")
weights = {wc1, wc2, wd1, Wout}
biases = {bc1, bc2, bd1, Bout}
print("NEXTArgmax")
prediction=tf.argmax(y,1)
probabilities = y
predY = prediction.eval(feed_dict={x: fvecs_np, y: labels_onehot}, session=sess)
probY = probabilities.eval(feed_dict={x: fvecs_np, y: labels_onehot}, session=sess)
accuracy = tf.reduce_mean(tf.cast(prediction, "float"))
print(sess.run(accuracy, feed_dict={x: fvecs_np, y: labels_onehot}))
print("%%%%%%%%%%%%%%%%%%%%%%%%%%")
print("Predicted::: ", predY, accuracy)
print("%%%%%%%%%%%%%%%%%%%%%%%%%%")
feed_dictTEST = {y: labels_onehot}
probabilities=probY
print("probabilities", probabilities.eval(feed_dict={x: fvecs_np}, session=sess))
########## Run Analysis ###########
TestModels()
So, when I run this code I get the correct prediction for the test set, although I am not sure I believe it, because it appears that whatever value I append in line 14 (see below) is the output it predicts:
labels.append(row[3])#<<<IT ALWAYS PREDICTS THIS VALUE!
I don't understand this, and it makes me suspicious that I've set up the CNN incorrectly, as I would have expected it to ignore my input label and determine a bast match from the trained network based on the trained patterns. The only thing I can figure is that when I pass the value through for the prediction; it is instead training the model on this data as well, and then predicting itself. Is this a correct assumption, or am I misinterpreting how Tensorflow works?
The other issue is that when I try to use code that (based on other tutorials) which is supposed to output the probabilities of all of the 2165 possible outputs, I get the error:
InvalidArgumentError (see above for traceback): Shape [-1,2165] has negative dimensions
[[Node: y = Placeholder[dtype=DT_FLOAT, shape=[?,2165], _device="/job:localhost/replica:0/task:0/cpu:0"]()]]
To me, it looks like it is the correct layer based on the 2165 value in the Tensor shape, but I don't understand the -1 value. So, to wrap up the summary, my questions are:
Based on the fact that I get the value that I have in the label of the input data, is this the correct method to make a classification using this model?
Am I missing a layer or have I configured the model incorrectly in order to extract the probabilities of all of the possible output classes, or am I using the wrong code to extract the information? I try to print out the accuracy to see if that would work, but instead it outputs the description of a tensor, so clearly that is incorrect as well.
(ADDITIONAL INFORMATION)
As requested, I'm also including the original code that was used to train the model, which is now below. You can see I do sort of a piece meal training of a limited number of related records at a time by their taxonomic relationships as I iterate through the file. This is mostly because the Mac that I'm training on (Mac Pro w/ 64GB ram) tends to give me the "Killed -9" error due to overuse of resources if I don't do it this way. There may be a better way to do it, but this seems to work.
Original Author: Aymeric Damien
Project: https://github.com/aymericdamien/TensorFlow-Examples/
from __future__ import print_function
import tensorflow as tf
import numpy as np
import csv
import random
# Parameters
num_epochs = 2
train_size = 1609
learning_rate = 0.001 #(larger >speed, lower >accuracy)
training_iters = 5000 # How much do you want to train (more = better trained)
batch_size = 32 #How many samples to train on, size of the training batch
display_step = 10 # How often to diplay what is going on during training
# Network Parameters
n_input = 10816 # MNIST data input (img shape: 28*28)...in my case 104x104 = 10816(rough array size)
n_classes = 2165 #3280 #2307 #787# Switched to 100 taxa/training set, dynamic was too wonky.
dropout = 0.75 # Dropout, probability to keep units. Jeffery Hinton's group developed it, that prevents overfitting to find new paths. More generalized model.
# Functions
def extract_data(filename):
print("extracting data...")
# arrays to hold the labels and feature vectors.
NUM_LABELS = 2165
NUM_FEATURES = 10826
taxCount = 0
taxCurrent = 0
labels = []
fvecs = []
rowCount = 0
#iterate over the rows, split the label from the features
#convert the labels to integers and features to floats
print("entering CNN loop")
for line in open(filename):
rowCount = rowCount + 1
row = line.split(',')
taxCurrent = row[3]
print("profile:", row[0:12])
labels.append(int(row[3]))
fvecs.append([float(x) for x in row [4:10820]])
#convert the array of float arrasy into a numpy float matrix
fvecs_np = np.matrix(fvecs).astype(np.float32)
#convert the array of int lables inta a numpy array
labels_np = np.array(labels).astype(dtype=np.uint8)
#convert the int numpy array into a one-hot matrix
labels_onehot = (np.arange(NUM_LABELS) == labels_np[:, None]).astype(np.float32)
print("arrays converted")
return fvecs_np, labels_onehot
# Create some wrappers for simplicity
def conv2d(x, W, b, strides=1): #Layer 1 : Convolutional layer
# Conv2D wrapper, with bias and relu activation
print("conv2d")
x = tf.nn.conv2d(x, W, strides=[1, strides, strides, 1], padding='SAME') # Strides are the tensors...list of integers. Tensors=data
x = tf.nn.bias_add(x, b) #bias is the tuning knob
return tf.nn.relu(x) #rectified linear unit (activation function)
def maxpool2d(x, k=2): #Layer 2 : Takes samples from the image. (This is a 4D tensor)
print("maxpool2d")
# MaxPool2D wrapper
return tf.nn.max_pool(x, ksize=[1, k, k, 1], strides=[1, k, k, 1],
padding='SAME')
# Create model
def conv_net(x, weights, biases, dropout):
print("conv_net setup")
# Reshape input picture
x = tf.reshape(x, shape=[-1, 104, 104, 1]) #-->52x52 , -->26x26x64
# Convolution Layer
conv1 = conv2d(x, weights['wc1'], biases['bc1']) #defined above already
# Max Pooling (down-sampling)
conv1 = maxpool2d(conv1, k=2)
print(conv1.get_shape)
# Convolution Layer
conv2 = conv2d(conv1, weights['wc2'], biases['bc2']) #wc2 and bc2 are just placeholders...could actually skip this layer...maybe
# Max Pooling (down-sampling)
conv2 = maxpool2d(conv2, k=2)
print(conv2.get_shape)
# Fully connected layer
# Reshape conv2 output to fit fully connected layer input
fc1 = tf.reshape(conv2, [-1, weights['wd1'].get_shape().as_list()[0]])
fc1 = tf.add(tf.matmul(fc1, weights['wd1']), biases['bd1'])
fc1 = tf.nn.relu(fc1) #activation function for the NN
# Apply Dropout
fc1 = tf.nn.dropout(fc1, dropout)
# Output, class prediction
out = tf.add(tf.matmul(fc1, weights['Wout']), biases['Bout'])
return out
def Train_Network(Txid_IN, Sess_File_Name):
import tensorflow as tf
tf.reset_default_graph()
x,y = 0,0
weights = {}
biases = {}
# tf Graph input
print("setting placeholders")
x = tf.placeholder(tf.float32, [None, n_input], name="x") #Gateway for data (images)
y = tf.placeholder(tf.float32, [None, n_classes], name="y") # Gateway for data (labels)
keep_prob = tf.placeholder(tf.float32) #dropout # Gateway for dropout(keep probability)
# Store layers weight & bias
#CREATE weights
weights = {
# 5x5 conv, 1 input, 32 outputs
'wc1': tf.Variable(tf.random_normal([5, 5, 1, 32]),name="wc1"), #
# 5x5 conv, 32 inputs, 64 outputs
'wc2': tf.Variable(tf.random_normal([5, 5, 32, 64]),name="wc2"),
# fully connected, 7*7*64 inputs, 1024 outputs
'wd1': tf.Variable(tf.random_normal([26*26*64, 1024]),name="wd1"),
# 1024 inputs, 10 outputs (class prediction)
'Wout': tf.Variable(tf.random_normal([1024, n_classes]),name="Wout")
}
biases = {
'bc1': tf.Variable(tf.random_normal([32]), name="bc1"),
'bc2': tf.Variable(tf.random_normal([64]), name="bc2"),
'bd1': tf.Variable(tf.random_normal([1024]), name="bd1"),
'Bout': tf.Variable(tf.random_normal([n_classes]), name="Bout")
}
# Construct model
print("constructing model")
pred = conv_net(x, weights, biases, keep_prob)
print(pred)
# Define loss(cost) and optimizer
#cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred, y)) Deprecated version of the statement
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits = pred, labels=y)) #added reduce_mean 6/27
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)
# Evaluate model
correct_pred = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
print("%%%%%%%%%%%%%%%%%%%%")
print ("%% ", correct_pred)
print ("%% ", accuracy)
print("%%%%%%%%%%%%%%%%%%%%")
# Initializing the variables
#init = tf.initialize_all_variables()
init = tf.global_variables_initializer()
saver = tf.train.Saver()
fvecs_np, labels_onehot = extract_data("MicroarrayDataOUT.csv") #CHAGE TO PICORNAVIRUS!!!!!AHHHHHH!!!
print("starting session")
# Launch the graph
FitStep = 0
with tf.Session() as sess: #graph is encapsulated by its session
sess.run(init)
step = 1
# Keep training until reach max iterations (training_iters)
while step * batch_size < training_iters:
if FitStep >= 5:
break
else:
#iterate and train
print(step)
print(fvecs_np, labels_onehot)
for step in range(num_epochs * train_size // batch_size):
sess.run(optimizer, feed_dict={x: fvecs_np, y: labels_onehot, keep_prob:dropout}) #no dropout???...added Keep_prob:dropout
if FitStep >= 5:
break
#else:
###batch_x, batch_y = mnist.train.next_batch(batch_size)
# Run optimization op (backprop)
###sess.run(optimizer, feed_dict={x: batch_x, y: batch_y,
### keep_prob: dropout}) <<<<SOMETHING IS WRONG IN HERE?!!!
if step % display_step == 0:
# Calculate batch loss and accuracy
loss, acc = sess.run([cost, accuracy], feed_dict={x: fvecs_np,
y: labels_onehot,
keep_prob: 1.})
print("Iter " + str(step*batch_size) + ", Minibatch Loss= " + \
"{:.6f}".format(np.mean(loss)) + ", Training Accuracy= " + \
"{:.5f}".format(acc))
TrainAcc = float("{:.5f}".format(acc))
#print("******", TrainAcc)
if TrainAcc >= .99: #Changed from .95 temporarily
print(FitStep)
FitStep = FitStep+1
saver.save(sess, Sess_File_Name, global_step=1000) #
print("Saved Session:", Sess_File_Name)
step += 1
print("Optimization Finished!")
print("Testing Accuracy:", \
sess.run(accuracy, feed_dict={x: fvecs_np[:256],
y: labels_onehot[:256],
keep_prob: 1.}))
#feed_dictTEST = {x: fvecs_np[50]}
#prediction=tf.argmax(y,1)
#print(prediction)
#best = sess.run([prediction],feed_dictTEST)
#print(best)
print("DONE")
sess.close()
def Tax_Iterator(CSV_inFile, CSV_outFile): #Deprecate
#Need to copy *.csv file to MySQL for sorting
resultFileINIT = open(CSV_outFile,'w')
resultFileINIT.close()
TaxCount = 0
TaxThreshold = 2165
ThresholdStep = 2165
PrevTax = 0
linecounter = 0
#Open all GenBank profile list
for line in open(CSV_inFile):
linecounter = linecounter+1
print(linecounter)
resultFile = open(CSV_outFile,'a')
wr = csv.writer(resultFile, dialect='excel')
# Check for new TXID
row = line.split(',')
print(row[7], "===", PrevTax)
if row[7] != PrevTax:
print("X1")
TaxCount = TaxCount+1
PrevTax = row[7]
#Check it current Tax count is < or > threshold
# < threshold
print(TaxCount,"=+=", TaxThreshold)
if TaxCount<=3300:
print("X2")
CurrentTax= row[7]
CurrTxCount = CurrentTax
print("TaxCount=", TaxCount)
print( "Add to CSV")
print("row:", CurrentTax, "***", row[0:15])
wr.writerow(row[0:-1])
# is > threshold
else:
print("X3")
# but same TXID....
print(row[7], "=-=", CurrentTax)
if row[7]==CurrentTax:
print("X4")
CurrentTax= row[7]
print("TaxCount=", TaxCount)
print( "Add to CSV")
print("row:", CurrentTax, "***", row[0:15])
wr.writerow(row[0:-1])
# but different TXID...
else:
print(row[7], "=*=", CurrentTax)
if row[7]>CurrentTax:
print("X5")
TaxThreshold=TaxThreshold+ThresholdStep
resultFile.close()
Sess_File_Name = "CNN_VirusIDvSPECIES_XXALL"+ str(TaxThreshold-ThresholdStep)
print("<<<< Start Training >>>>"
print("Training on :: ", CurrTxCount, "Taxa", TaxCount, "data points.")
Train_Network(CurrTxCount, Sess_File_Name)
print("Training complete")
resultFileINIT = open(CSV_outFile,'w')
resultFileINIT.close()
CurrentTax= row[7]
#reset tax count
CurrTxCount = 0
TaxCount = 0
resultFile.close()
Sess_File_Name = "MicroarrayCNN_Data"+ str(TaxThreshold+ThresholdStep)
print("<<<< Start Training >>>>")
print("Training on :: ", CurrTxCount, "Taxa", TaxCount, "data points.")
Train_Network(CurrTxCount, Sess_File_Name)
resultFileINIT = open(CSV_outFile,'w')
resultFileINIT.close()
CurrentTax= row[7]
Tax_Iterator("MicroarrayInput.csv", "MicroarrayOutput.csv")
You defined prediction as prediction=tf.argmax(y,1). And in both feed_dict, you feed labels_onehot for y. Consequently, your "prediction" is always equal to the labels.
As you didn't post the code you used to train your network, I can't tell you what exactly you need to change.
Edit: I have isses understanding the underlying problem you're trying to solve - based on your code, you're trying to train a neural network with 2165 different classes using 1609 training examples. How is this even possible? If each example had a different class, there would still be some classes without any training example. Or does one image belong to many classes? From your statement at the beginning of your question, I had assumed you're trying to output a real-valued number between 0-1.
I'm actually surprised that the code actually worked as it looks like you're adding only a single number to your labels list, but your model expects a list with length 2165 for each training example.