I created a custom CrossValidator with SplitType as Random/Time-based/Stratified based on this reference. When SplitType == Random, I use the original _kFold function of CrossValidator in pyspark.ml to test the result of my custom CrossValidator with _fit function as below.
I expected when SplitType == Random, the results should be close to what original Sparkml CrossValidator produce but even after multiple runs, orignal SparkML CrossValidator gives always better results. Anyone can help me to understand the reasons please?
def _fit(self, dataset : DataFrame):
est = self.getOrDefault(self.estimator)
epm = self.getOrDefault(self.estimatorParamMaps)
numModels = len(epm)
eva = self.getOrDefault(self.evaluator)
nFolds = self.getOrDefault(self.numFolds)
metrics_all = [[0.0] * numModels for i in range(nFolds)]
SplitType = self.getOrDefault(self.SplitType)
pool = ThreadPool(processes=min(self.getParallelism(), numModels))
subModels = None
collectSubModelsParam = self.getCollectSubModels()
if collectSubModelsParam:
subModels = [[None for j in range(numModels)] for i in range(nFolds)]
if SplitType == 'Random':
datasets = self._kFold(dataset)
print(SplitType)
elif SplitType == 'Stratified':
datasets = self.stratify_data(dataset)
print(SplitType)
for i in range(nFolds):
validation = datasets[i][1].cache()
train = datasets[i][0].cache()
tasks = map(
inheritable_thread_target,
_parallelFitTasks(est, train, eva, validation, epm, collectSubModelsParam),
)
for j, metric, subModel in pool.imap_unordered(lambda f: f(), tasks):
metrics_all[i][j] = metric
if collectSubModelsParam:
assert subModels is not None
subModels[i][j] = subModel
validation.unpersist()
train.unpersist()
metrics, std_metrics = CrossValidator._gen_avg_and_std_metrics(metrics_all)
if eva.isLargerBetter():
bestIndex = np.argmax(metrics)
else:
bestIndex = np.argmin(metrics)
bestModel = est.fit(dataset, epm[bestIndex])
return self._copyValues(
CrossValidatorModel(bestModel, metrics, cast(List[List[Model]], subModels), std_metrics)
)
With original CrossValidator, the result is always much more stable and better, even after multiple runs :
lr = LogisticRegression(featuresCol = 'features', labelCol = 'label')
grid = ParamGridBuilder().addGrid(lr.maxIter, [0, 1, 5]).build()
evaluator = BinaryClassificationEvaluator()
scv = CustomCrossValidator(
estimator=lr,
estimatorParamMaps=grid,
evaluator=evaluator,
numFolds=4,
SplitType='Random'
)
Origin CrossValidator :
avgMetrics
stdMetrics
0.5
0
0.8457
0.0425
0.8457
0.0425
My Custom CrossValidator with Random SplitType :
avgMetrics
stdMetrics
0.5
0
0.8278
0.0937
0.8278
0.0937
Anyone can help to explain the reasons please?
Many thanks,
Related
I am new to stackoverflow, so I apologize for any errors while asking a question. I am trying to solve the cartpole-v1 gym environment using a dqn agent. I am facing an issue as follows ValueError: Weights for model sequential have not yet been created. Weights are created when the Model is first called on inputs or build() is called with an input_shape. I've searched how to fix this but to no success. My tensorflow version is 2.8.0. My code for my agent is as follows. I believe, the problem is most probably due to my build_model and in the model.fit line. This is the error that I am facing
class DQNAgent0:
def __init__(self, state_size, action_size):
self.state_size = state_size
self.action_size = action_size
self.memory = deque(maxlen=2000)
self.gamma = 0.95 # discount factor
self.epsilon = 1.0 # 100% exploration at the start
self.epsilon_decay = 0.995
self.epsilon_min = 0.01
self.learning_rate = 0.001
self.model = self._build_model()
def _build_model(self):
'''model = tf.keras.Sequential([
tf.keras.layers.Dense(1),
#tf.keras.Input((self.state_size,)),
tf.keras.layers.Dense(24, activation="relu"),
tf.keras.layers.Dense(24, activation="relu"),
tf.keras.layers.Dense(self.action_size, activation="linear"),
])
model.compile(loss=tf.keras.losses.mse,
optimizer=tf.keras.optimizers.Adam(learning_rate=self.learning_rate))'''
#model = tf.keras.Sequential()
model = tf.keras.Sequential([tf.keras.layers.Dense(1)])
model.add(tf.keras.Input(shape = self.state_size))
model.add(tf.keras.layers.Dense(24, activation = 'relu'))
model.add(tf.keras.layers.Dense(24, activation = 'relu'))
model.add(tf.keras.layers.Dense(self.action_size, activation = 'linear'))
#opt = tf.keras.optimizers.Adam(learning_rate = self.learning_rate)
#model.compile(loss = 'mse', optimizer = opt)
model.compile(loss = tf.keras.losses.mse, optimizer = tf.keras.optimizers.Adam(learning_rate = self.learning_rate))
return model
def remember(self, state, action, reward, next_state, done):
self.memory.append((state, action, reward, next_state, done))
def act(self, state):
if np.random.rand() <= self.epsilon:
return random .randrange(self.action_size) # exploratory action
act_values = self.model.predict(state)
return np.argmax(act_values[0])
def replay(self, batch_size):
#creating a random sample from our memory
minibatch = random.sample(self.memory, batch_size)
for state, action, reward, next_state, done in minibatch:
target = reward
if not done:
target = (reward + self.gamma * np.amax(self.model.predict(next_state[0]))) # reward at current timestep + discounted future reward
target_f = self.model.predict(state)
target_f[0][action] = target #mapping future reward to the current reward
self.model.fit(tf.expand_dims(state, axis=-1), target_f, epochs = 1, verbose = 0) # fitting a model to train with state as input x and target_f as y (predicted future reward)
if self.epsilon > self.epsilon_min:
self.epsilon *= self.epsilon_decay
def load(self, name):
self.model.load_weights(name)
def save(self, name):
self.model.save_weights(name)
I'm trying to build a Pytorch network for image captioning.
Currently I have a working network of Encoder and Decoder, and I want to add nn.MultiheadAttnetion layer to it (to be used as self attention).
Currently my decode looks like this:
class Decoder(nn.Module):
def __init__(self, hidden_size, embed_dim, vocab_size, layers = 1):
super(Decoder, self).__init__()
self.embed_dim = embed_dim
self.vocab_size = vocab_size
self.layers = layers
self.hidden_size = hidden_size
self.embedding = nn.Embedding(vocab_size, embed_dim, padding_idx=0)
self.lstm = nn.LSTM(input_size = embed_dim, hidden_size = hidden_size, batch_first = True, num_layers = layers)
#self.attention = nn.MultiheadAttention(hidden_size, num_heads=1, batch_first= True)
self.fc = nn.Linear(hidden_size, self.vocab_size)
def init_hidden(self, batch_size):
h = torch.zeros(self.layers, batch_size, self.hidden_size).to(device)
c = torch.zeros(self.layers, batch_size, self.hidden_size).to(device)
return h,c
def forward(self, features, caption):
batch_size = caption.size(0)
caption_size = caption.size(1)
h,c = self.init_hidden(batch_size)
embeddings = self.embedding(caption)
lstm_input = torch.cat((features.unsqueeze(1), embeddings[:,:-1,:]), dim=1)
output, (h,c) = self.lstm(lstm_input, (h,c))
#output, _ = self.attention(output, output, output)
output = self.fc(output)
return output
def generate_caption(self, features, max_caption_size = MAX_LEN):
h,c = self.init_hidden(1)
caption = ""
embeddings = features.unsqueeze(1)
for i in range(max_caption_size):
output, (h, c) = self.lstm(embeddings, (h,c))
#output, _ = self.attention(output, output, output)
output = self.fc(output)
_, word_index = torch.max(output, dim=2) # take the word with highest probability
if word_index == vocab.get_index(END_WORD):
break
caption += vocab.get_word(word_index) + " "
embeddings = self.embedding(torch.LongTensor([word_index]).view(1,-1).to(device))
return caption
and it gives relatively good results for image captioning.
I want to add the commented out lines so the model will use Attention. But- when I do that- the model breaks, although the loss becomes extremely low (decreasing from 2.7 to 0.2 during training instead of 2.7 to 1 without the attention) - the caption generation is not really working (predicts the same word over and over again).
My questions are:
Am I using the nn.MultiheadAttention correctly? it is very weird to me that it should be used after the LSTM, but I saw this online, and it works from dimension sizes perspective
Any idea why my model breaks when I use Attention?
EDIT: I also tried to put the Attention before the LSTM, and it didn't work as well (network predicted the same caption for every picture)
I am using a Logistic Regression model on PySpark through databricks but i am not able to get my precision and recall. Everything works fine and I am able to get my ROC but there is not attribute or lib for Precision and Recall
lrModel = LogisticRegression()
predictions = bestModel.transform(testData)
# Instantiate metrics object
results = predictions.select(['probability', 'label'])
results_collect = results.collect()
results_list = [(float(i[0][0]), 1.0-float(i[1])) for i in results_collect]
scoreAndLabels = sc.parallelize(results_list)
metrics = MulticlassMetrics(scoreAndLabels)
# Overall statistics
precision = metrics.precision()
recall = metrics.recall()
f1Score = metrics.fMeasure()
print("Summary Stats")
print("Precision = %s" % precision)
print("Recall = %s" % recall)
print("F1 Score = %s" % f1Score)
>>>Summary Stats
>>>Precision = 0.0
>>>Recall = 0.0
>>>F1 Score = 0.0
I was able to create my own function to do so. It returns everything and more. I am using the "MulticlassMetrics()" from mllib package. Since its a multiclass it calculates metrics for each label so, you have to specify which label you want to retrieve.
### Model Evaluator User Defined Functions
def udfModelEvaluator(dfPredictions, labelColumn='label'):
colSelect = dfPredictions.select(
[F.col('prediction').cast(DoubleType())
,F.col(labelColumn).cast(DoubleType()).alias('label')])
metrics = MulticlassMetrics(colSelect.rdd)
mAccuracy = metrics.accuracy
mPrecision = metrics.precision(1)
mRecall = metrics.recall(1)
mF1 = metrics.fMeasure(1.0, 1.0)
mMatrix = metrics.confusionMatrix().toArray().astype(int)
mTP = metrics.confusionMatrix().toArray()[1][1]
mTN = metrics.confusionMatrix().toArray()[0][0]
mFP = metrics.confusionMatrix().toArray()[0][1]
mFN = metrics.confusionMatrix().toArray()[1][0]
mResults = [mAccuracy, mPrecision, mRecall, mF1, mMatrix, mTP, mTN, mFP, mFN, "Return [[0]=Accuracy, [1]=Precision, [2]=Recall, [3]=F1, [4]=ConfusionMatrix, [5]=TP, [6]=TN, [7]=FP, [8]=FN]"]
return mResults
To call the function:
metricsList = udfModelEvaluator(predictionsData, "label")
metricsList
I am new to CNN and tried to train the CNN model. However when I try to print the accuracies returned from cnn it gives me results in bytes format like b'\n\x11\n\naccuracy_1\x15\x00\x00\x80<'. However when I try to print the values from the loss_train obtained from the same sess.run I get value of 1419.06. Why is this happening.
########################################################################################################################
#IMPORT PACKAGES
import math
import shutil
import pywt
import sys
import random
import numpy as np
import h5py
import pip
import os
from os import system
import tensorflow as tf
from PIL import Image
import matplotlib
import matplotlib.pyplot as plt
import skimage.io as io
import matplotlib.image as mpimg
import time
np.random.seed(1)
slim = tf.contrib.slim
########################################################################################################################
########################################################################################################################
#The FLAGS are used to assign constant values to several paths as well as variables that will be constantly used.
flags = tf.app.flags
flags.DEFINE_string('dataset_dir','E:\\CODING\\CNN_Compressed\\Trial\\Codes\\using_numpy\\NWPU-RESISC45\\NWPU-RESISC45\\','E:\\CODING\\CNN_Compressed\\Trial\\Codes\\using_numpy\\NWPU-RESISC45\\NWPU-RESISC45\\')
flags.DEFINE_float('validation_size', 0.1, 'Float: The proportion of examples in the dataset to be used for validation')
flags.DEFINE_float('test_size', 0.1, 'Float: The proportion of examples in the dataset to be used for test')
flags.DEFINE_integer('num_shards', 1, 'Int: Number of shards to split the TFRecord files into')
flags.DEFINE_integer('random_seed', 0, 'Int: Random seed to use for repeatability.')
flags.DEFINE_string('tfrecord_filename', None, 'String: The output filename to name your TFRecord file')
tf.app.flags.DEFINE_integer('target_image_height', 256, 'train input image height')
tf.app.flags.DEFINE_integer('target_image_width', 256, 'train input image width')
tf.app.flags.DEFINE_integer('batch_size', 128, 'batch size of training.')
tf.app.flags.DEFINE_integer('num_epochs', 30, 'epochs of training.')
tf.app.flags.DEFINE_float('learning_rate', 0.001, 'learning rate of training.')
FLAGS = flags.FLAGS
img_size = 256
num_channels=3
num_classes=45
########################################################################################################################
########################################################################################################################
datapath_train = 'E:\\CODING\\CNN_Compressed\\Trial\\Codes\\using_numpy\\NWPU-RESISC45\\NWPU-RESISC45\\train\\None_train_00000-of-00001.tfrecord'
def _extract_fn(tfrecord):
features={
'image/encoded': tf.FixedLenFeature([], tf.string),
'image/format': tf.FixedLenFeature([], tf.string),
'image/class/label': tf.FixedLenFeature([], tf.int64),
'image/height': tf.FixedLenFeature([], tf.int64),
'image/width': tf.FixedLenFeature([], tf.int64),
'image/channels': tf.FixedLenFeature([],tf.int64)
}
parsed_example = tf.parse_single_example(tfrecord, features)
image_de = tf.io.decode_raw(parsed_example['image/encoded'],tf.uint8)
img_height = tf.cast(parsed_example['image/height'],tf.int32)
img_width = tf.cast(parsed_example['image/width'],tf.int32)
img_channel = tf.cast(parsed_example['image/channels'],tf.int32)
img_shape = tf.stack([img_height,img_width,img_channel])
label = tf.cast(parsed_example['image/class/label'],tf.int64)
image = tf.reshape(image_de,img_shape)
#label = parsed_example['image/class/label']
return image, img_shape, label
########################################################################################################################
#########################################################################################################################
"""
# Pipeline of dataset and iterator
dataset = tf.data.TFRecordDataset(datapath)
# Parse the record into tensors.
dataset = dataset.map(_extract_fn)
# Generate batches
dataset = dataset.batch(1)
# Create a one-shot iterator
iterator = dataset.make_one_shot_iterator()
image, img_shape, label = iterator.get_next()
with tf.Session() as sess:
try:
print(sess.run(img_shape))
image_batch=sess.run(image)
print(image_batch)
img_bas=tf.cast(image_batch,tf.uint8)
plt.imshow(image_batch[0,:,:,:]*255)
plt.show()
except tf.errors.OutOfRangeError:
pass"""
########################################################################################################################
########################################################################################################################
#INITIALIZATION FOR THE CNN ARCHITECTURE
filter_size_conv1 = [5,5]
num_filters_conv1 = 32
filter_shape_pool1 = [2,2]
filter_size_conv2 = [3,3]
num_filters_conv2 = 64
filter_shape_pool2 = [2,2]
#PLACEHOLDERS
x = tf.placeholder(tf.float32, shape = [None, img_size,img_size,num_channels], name='x')
y = tf.placeholder(tf.int32, shape= [None], name = 'ytrue') #Output data placeholder
y_one_hot = tf.one_hot(y,45)
y_true_cls = tf.argmax(y_one_hot, dimension=1)
########################################################################################################################
########################################################################################################################
def new_conv_layer(input, num_input_channels, filter_size, num_filters, name):
with tf.variable_scope(name) as scope:
# Shape of the filter-weights for the convolution
shape = [filter_size, filter_size, num_input_channels, num_filters]
# Create new weights (filters) with the given shape
weights = tf.Variable(tf.truncated_normal(shape, stddev=0.05))
# Create new biases, one for each filter
biases = tf.Variable(tf.constant(0.05, shape=[num_filters]))
# TensorFlow operation for convolution
layer = tf.nn.conv2d(input=input, filter=weights, strides=[1, 1, 1, 1], padding='SAME')
# Add the biases to the results of the convolution.
layer += biases
return layer, weights
def new_pool_layer(input, name):
with tf.variable_scope(name) as scope:
# TensorFlow operation for convolution
layer = tf.nn.max_pool(value=input, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
return layer
def new_relu_layer(input, name):
with tf.variable_scope(name) as scope:
# TensorFlow operation for convolution
layer = tf.nn.relu(input)
return layer
def new_fc_layer(input, num_inputs, num_outputs, name):
with tf.variable_scope(name) as scope:
# Create new weights and biases.
weights = tf.Variable(tf.truncated_normal([num_inputs, num_outputs], stddev=0.05))
biases = tf.Variable(tf.constant(0.05, shape=[num_outputs]))
# Multiply the input and weights, and then add the bias-values.
layer = tf.matmul(input, weights) + biases
return layer
# CONVOLUTIONAL LAYER 1
layer_conv1, weights_conv1 = new_conv_layer(input=x, num_input_channels=3, filter_size=5, num_filters=32, name ="conv1")
# Pooling Layer 1
layer_pool1 = new_pool_layer(layer_conv1, name="pool1")
# RelU layer 1
layer_relu1 = new_relu_layer(layer_pool1, name="relu1")
# CONVOLUTIONAL LAYER 2
layer_conv2, weights_conv2 = new_conv_layer(input=layer_relu1, num_input_channels=32, filter_size=5, num_filters=64, name= "conv2")
# Pooling Layer 2
layer_pool2 = new_pool_layer(layer_conv2, name="pool2")
# RelU layer 2
layer_relu2 = new_relu_layer(layer_pool2, name="relu2")
# FLATTEN LAYER
num_features = layer_relu2.get_shape()[1:4].num_elements()
layer_flat = tf.reshape(layer_relu2, [-1, num_features])
# FULLY-CONNECTED LAYER 1
layer_fc1 = new_fc_layer(layer_flat, num_inputs=num_features, num_outputs=1000, name="fc1")
# RelU layer 3
layer_relu3 = new_relu_layer(layer_fc1, name="relu3")
# FULLY-CONNECTED LAYER 2
layer_fc2 = new_fc_layer(input=layer_relu3, num_inputs=1000, num_outputs=45, name="fc2")
# Use Softmax function to normalize the output
with tf.variable_scope("Softmax"):
y_pred = tf.nn.softmax(layer_fc2)
y_pred_cls = tf.argmax(y_pred, dimension=1)
# Use Cross entropy cost function
with tf.name_scope("cross_ent"):
cross_entropy = tf.nn.softmax_cross_entropy_with_logits_v2(logits=layer_fc2, labels=y_one_hot)
cost = tf.reduce_mean(cross_entropy)
# Use Adam Optimizer
with tf.name_scope("optimizer"):
optimizer = tf.train.AdamOptimizer(learning_rate = 1e-4).minimize(cost)
# Accuracy
with tf.name_scope("accuracy"):
correct_prediction = tf.equal(y_pred_cls, y_true_cls)
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
# setup the initialisation operator
init_op = tf.global_variables_initializer()
# Pipeline of dataset and iterator
dataset_train = tf.data.TFRecordDataset(datapath_train)
# Parse the record into tensors.
dataset_train = dataset_train.map(_extract_fn)
# Generate batches
dataset_train = dataset_train.batch(FLAGS.batch_size)
iterator_train = dataset_train.make_initializable_iterator()
next_element_train = iterator_train.get_next()
print('\n Starting the CNN train')
# Initialize the FileWriter
writer_train = tf.summary.FileWriter("Training_FileWriter/")
writer_val = tf.summary.FileWriter("Validation_FileWriter/")
#summary
accuracy = tf.summary.scalar("accuracy", accuracy)
loss = tf.summary.scalar("loss", cost)
# Merge all summaries together
merged_summary = tf.summary.merge_all()
#PERFORM THE CNN OPERATIONS
with tf.Session() as sess:
sess.run(init_op)
sess.run(iterator_train.initializer)
# Add the model graph to TensorBoard
writer_train.add_graph(sess.graph)
writer_val.add_graph(sess.graph)
# Loop over number of epochs
print('\nTraining')
for epoch in range(FLAGS.num_epochs):
sess.run(iterator_train.initializer)
start_time = time.time()
train_accuracy = 0
validation_accuracy = 0
acc_train_avg = 0
val_acc_avg = 0
for batch in range(0, int(25200/FLAGS.batch_size)):
img_train, shp_train, lbl_train = sess.run(next_element_train)
_, loss_train, acc_train, acc_summ = sess.run([optimizer, cost, accuracy, merged_summary], feed_dict = {x: img_train, y: lbl_train})
print(loss_train)
print(acc_train)
train_accuracy+=acc_train
end_time = time.time()
#acc_train_avg = (train_accuracy/(int(25200/FLAGS.batch_size)))
#TRAINING
print("Epoch "+str(epoch+1)+" completed : Time usage "+str(int(end_time-start_time))+" seconds")
print("\tAccuracy:")
print("\t- Training Loss:\t{}", loss_train)
print ("\t- Training Accuracy:\t{}",acc_train)
writer_train.add_summary(acc_summ,epoch+1)
#######################################################################################################################
The error is obtained as
Training
1427.1069
b'\n\x11\n\naccuracy_1\x15\x00\x00\x80<'
Traceback (most recent call last):
File "train_trial.py", line 302, in <module>
train_accuracy+=acc_train
TypeError: unsupported operand type(s) for +=: 'int' and 'bytes'
You are overwriting your loss and accuracy operations here:
accuracy = tf.summary.scalar("accuracy", accuracy)
loss = tf.summary.scalar("loss", cost)
Then when you run accuracy you get the protobuf bytes of the summary, instead of just running the op. You should rename these variables to prevent overwriting/name clashes.
I want use keras Lstm to get the time series features, then use the features to Kmeans. But now I can not get the layers output values. How can I get the layers output values?
This is my lstm network
Layer (type) Output Shape Param #
lstm_66 (LSTM) (None, None, 50) 10400
lstm_67 (LSTM) (None, 100) 60400
dense_19 (Dense) (None, 1) 101
activation_19 (Activation) (None, 1) 0
I want to get the lstm_67 output values,my code is:
import keras.backend as K
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
import tensorflow as tf
sess = tf.Session()
sess.run(tf.global_variables_initializer())
import numpy as np
statesAll=[]
layers = model.layers
print layers[1].output,type(layers[1].output[1]),sess.run(layers[1].output)
and the result is:
Tensor("lstm_61/TensorArrayReadV3:0", shape=(?, 100), dtype=float32)
So, how can I get the layers output value?
Thanks!
But it not work,my code is:
def load_data(file_name, sequence_length=10, split=0.8):
df = pd.read_csv(file_name, sep=',', usecols=[1])
data_all = np.array(df).astype(float)
scaler = MinMaxScaler()
data_all = scaler.fit_transform(data_all)
data = []
print len(data_all)
for i in range(len(data_all) - sequence_length - 1):
data.append(data_all[i: i + sequence_length + 1])
reshaped_data = np.array(data).astype('float64')
np.random.shuffle(reshaped_data)
x = reshaped_data[:, :-1]
y = reshaped_data[:, -1]
split_boundary = int(reshaped_data.shape[0] * split)
train_x = x[: split_boundary]
test_x = x[split_boundary:]
train_y = y[: split_boundary]
test_y = y[split_boundary:]
return train_x, train_y, test_x, test_y, scaler
def build_model(n_samples, time_steps, input_dim):
model = Sequential()
model.add(LSTM(input_dim=1, output_dim=50,return_sequences=True))
model.add(LSTM(100, return_sequences=False))
model.add(Dense(output_dim=1))
model.add(Activation('linear'))
model.compile(loss='mse', optimizer='rmsprop')
print(model.layers)
return model
def train_model(train_x, train_y, test_x, test_y):
model = build_model()
model.fit(train_x, train_y, batch_size=128, nb_epoch=30,validation_split=0.1)
return model
train_x, train_y, test_x, test_y, scaler = load_data(file path)
train_x = np.reshape(train_x, (train_x.shape[0], train_x.shape[1], 1))
test_x = np.reshape(test_x, (test_x.shape[0], test_x.shape[1], 1))
model = train_model(train_x, train_y, test_x, test_y)
from keras import backend as K
layers = model.layers
K.eval(layers[1].output)
In TensorFlow 2.x, you can do like this:
from tensorflow.python.keras import backend as K
model = build_model()
# lstm_67 is the second layer.
lstm = K.function([model.layers[0].input], [model.layers[1].output])
lstm_output = lstm([test_x])[0]
keras.backend.eval() should do.
Look at the documentation here and here
First of all, this is a tensor, you need to use the tf. Print () method to see the specific value. If you use Spyder, you will not see this information in the console. You need to execute this program in the command line.