Develop Reference

TypeError: Cannot iterate over a Tensor with unknown first dimension

Getting the following error when I execute the below code:
TypeError: Cannot iterate over a Tensor with unknown first dimension.
How to solve this? The error is in the line output_gcn = gcn(input_layer)
I tried reshaping the input_layer, but it didnt work
What is the problem and how to solve it?
Please let me know the solution as early as possible, as I am doing something apart from learning and have deadlines to meet
import numpy as np
from sklearn.metrics.pairwise import cosine_similarity
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.model_selection import train_test_split
from stellargraph.data import UniformRandomWalk
#from stellargraph.layer import GCN
from stellargraph import StellarGraph
from tensorflow.keras import layers, Model, optimizers
from stellargraph.mapper import FullBatchNodeGenerator
from stellargraph.layer import GCN
from stellargraph.layer import node2vec
from stellargraph import StellarGraph
#from stellargraph.draw import draw
#generator = PaddedGraphGenerator(graphs=graphs)`
pro_tweets = pprocess[0:10000]
labels = df_encoded[['label_mild', 'label_moderate', 'label_non-depressed',
'label_severe']]
np.array(labels)
vectorizer = TfidfVectorizer()
vectors = vectorizer.fit_transform(pro_tweets)
#print(vectors)
print(vectors.shape)
similarity_matrix = cosine_similarity(vectors)
adj_matrix = np.zeros(similarity_matrix.shape)
adj_matrix[similarity_matrix > 0] = similarity_matrix[similarity_matrix > 0]
#print(adj_matrix)
#print(adj_matrix.shape[0])
graph = StellarGraph(adj_matrix, node_features=vectors)
rw = UniformRandomWalk(graph)
walks = rw.run(nodes=list(range(adj_matrix.shape[0])), length=5, n=1)
gcn = GCN(layer_sizes=[32, 16], activations=["relu", "relu"], generator =
FullBatchNodeGenerator(graph, method="gcn"))
#input_layer = GCN.get_input_layer(graph)
input_layer = layers.Input(shape = (vectors.shape[1],), dtype="float32", name="input")
print(input_layer.shape)
print(input_layer)
#reshaped_input_layer = tf.reshape(input_layer, [vectors.shape[1],])
import tensorflow as tf
output_gcn = gcn(input_layer)
#input_layer = layers.Input(shape=(adj_matrix.shape[0],adj_matrix.shape[1]),
dtype="int32", name="input")
#output_layer = gcn(input_layer)
output_embedding = node2vec(output_dim=16)(output_gcn)
dense_layer = layers.Dense(16, activation="relu")(output_embedding)
output_layer = layers.Dense(4, activation="softmax")(dense_layer)
'''create the final dense layer
dense_layer = layers.Dense(16, activation="relu")(output_layer)
output_layer = layers.Dense(1, activation="sigmoid")(dense_layer)'''
model = Model(inputs=input_layer, outputs=output_layer)
model.compile(optimizer=optimizers.Adam(lr=0.01), loss="binary_crossentropy", metrics=
["acc"])
X_train, X_test, y_train, y_test = train_test_split(walks, labels, test_size=0.2,
random_state=42)
model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=50)
test_predictions = model.predict(X_test)
test_predictions = np.round(test_predictions)
accuracy = (test_predictions == y_test).mean()
print("Accuracy: {:.4f}".format(accuracy))
train_predictions = model.predict(X_train)
train_predictions = np.round(train_predictions)
accuracy = (train_predictions == y_train).mean()
print("Accuracy: {:.4f}".format(accuracy))]

LSTM model has poor prediction in simple example

I am trying to generate a LSTM model using Keras. I create a simple sine wave example which contain more thang 1000 point to predict the next point. But the result is not good as i expected. When i fit the model the result is moves between 0~1 not like the sine wave. I have tried to change parameter like epoch, batchsize, learning rate, but it is not better.
model predict image
What am I doing wrong?
import joblib
import numpy as np
import matplotlib.pyplot as plt
import copy
import gc
import os
import sys
from sklearn.preprocessing import MinMaxScaler
from tensorflow import keras
import tensorflow as tf
from tensorflow.keras.models import *
from tensorflow.keras.layers import *
from keras.callbacks import Callback
learning_rate = 0.001
len_train = 30
total_predict = 300
len_test = 400
epoch = 100
batch_size = 32
workers = -1
class Callback_Class(Callback):
def load_data(self, x_test, y_test):
self.x_test = x_test
self.y_test = np.array(y_test)
def model_predict(self, data_close):
output_predict = []
for i in range(total_predict):
if (i==0):
data_close_ = data_close.reshape(-1, len_train, 1)
else:
data_close_ = np.delete(data_close_, 0)
data_close_ = np.append(data_close_, pred_close)
data_close_ = data_close_.reshape(-1, len_train, 1)
pred_close = model.predict(data_close_)
pred_close = pred_close.ravel()
pred_close = np.array(pred_close).reshape(len(pred_close), 1)
pred_cl = sc.inverse_transform(pred_close)
output_predict.append(pred_cl)
output_predict = np.array(output_predict)
return output_predict
def on_epoch_end(self, epoch, logs=None):
if (epoch % 20 == 0):
output_predict = self.model_predict(self.x_test)
fig, ax = plt.subplots(figsize=(12,6))
ax.grid(True)
plt.title(f"Model predict")
plt.plot(output_predict.ravel(), color="red", label='Predict')
plt.plot(self.y_test.ravel(), color="blue", label='REAL')
fig.tight_layout()
plt.legend(loc='lower left')
plt.savefig(f'Demo_lstm_epoch_{epoch}.png')
plt.clf()
plt.close()
def lstm_reg(input_shape=(60, 1), unit=40, clustering_params=None):
inputs = Input(input_shape)
lstm1f = Bidirectional(LSTM(units=32, return_sequences=True))(inputs)
lstm1f = Bidirectional(LSTM(units=32, return_sequences=False))(lstm1f)
outputs = Dense(units=1, activation='linear')(lstm1f)
model = Model(inputs=inputs, outputs=outputs)
model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=learning_rate), loss='mean_squared_error', metrics=["accuracy"])
return model
def create_data_train(data_time_series):
data_time_series = np.array(data_time_series).ravel()
X_train = []
y_train = []
for i in range(len_train, len(data_time_series)):
X_train.append(data_time_series[i-len_train:i])
y_train.append(data_time_series[i])
X_train, y_train = np.array(X_train), np.array(y_train)
X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1))
return X_train, y_train
x = np.linspace(-20*np.pi, 20*np.pi, 2001)
sin_alpha = np.sin(x).ravel()
sin_alpha_train = np.array(copy.deepcopy(sin_alpha))[:len(sin_alpha)-len_test]
sin_alpha_train = np.array(sin_alpha_train).reshape(len(sin_alpha_train), 1)
sc = MinMaxScaler(feature_range=(0, 1))
sin_alpha_train = sc.fit_transform(sin_alpha_train)
X_train, y_train = create_data_train(sin_alpha_train)
joblib.dump(sc, f'Demo_MinMaxScaler.gz')
sc = joblib.load(f"Demo_MinMaxScaler.gz")
X_test = np.array(copy.deepcopy(sin_alpha))[len(sin_alpha)-len_test:len(sin_alpha)-len_test+len_train]
X_test = np.array(X_test).reshape(len(X_test), 1)
X_test = sc.fit_transform(X_test)
y_test = np.array(copy.deepcopy(sin_alpha))[len(sin_alpha)-len_test+len_train:len(sin_alpha)-len_test+len_train+total_predict]
model = lstm_reg(input_shape=(len_train, 1), unit=int(2*(len_train+len(y_train))/3))
model.summary()
callback_class = Callback_Class()
callback_class.load_data(X_test, y_test)
model.fit(X_train, y_train, epochs=epoch, use_multiprocessing=True, verbose=1, callbacks=[callback_class], workers=workers, batch_size=batch_size)

It seems like you are normalizing your features and your labels in these lines
sc = MinMaxScaler(feature_range=(0, 1))
sin_alpha_train = sc.fit_transform(sin_alpha_train)
X_train, y_train = create_data_train(sin_alpha_train)
Try it without scaling your label set. Due to your output layer using the linear activation function, which is correct as you're working on a regression problem, the model should be able to handle non scaled labels. The model only learns your data in a range of 0 to 1 while your sine wave goes from -1 to 1.

How to integrate keras model with sequential backward selection code?

I am trying to integrate a Keras deep neural network as a classifier within code for sequential backward feature selection in Python. (Originally, I tried to wrap the Keras deep neural network within Scikeras to use within scikit-learn's built in sequential feature selection models, but I kept getting error messages).
I found this code from scratch for sequential backward feature selection (taken from https://vitalflux.com/sequential-backward-feature-selection-python-example/), and have been trying to integrate a Keras model in the code to replace the "estimator" within the function but I keep getting this error: ValueError: Input 0 of layer "sequential_410" is incompatible with the layer: expected shape=(None, 45), found shape=(None, 44)
Here is the code that I have so far for the sequential backward feature selection and the deep neural network:
import pandas as pd
import numpy as np
from tensorflow import keras
from tensorflow.keras import layers
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation
from keras.optimizers import Adam
from scikeras.wrappers import KerasClassifier, KerasRegressor
# SBS (sequential backward feature selection) from scratch
#=====================================================
from sklearn.metrics import accuracy_score
from itertools import combinations
from sklearn.base import clone
class SequentialBackwardSearch():
'''
Instantiate with Estimator and given number of features
'''
def __init__(self, estimator, k_features):
self.estimator = clone(estimator)
self.k_features = k_features
'''
X_train - Training data Pandas dataframe
X_test - Test data Pandas dataframe
y_train - Training label Pandas dataframe
y_test - Test data Pandas dataframe
'''
def fit(self, X_train, X_test, y_train, y_test):
dim = X_train.shape[1]
self.indices_ = tuple(range(dim))
self.subsets_ = [self.indices_]
score = self._calc_score(X_train.values, X_test.values,
y_train.values, y_test.values, self.indices_)
self.scores_ = [score]
'''
Iterate through all the dimensions until k_features is reached
At the end of loop, dimension count is reduced by 1
'''
while dim > k_features:
scores = []
subsets = []
'''
Iterate through different combinations of features, train the model,
record the score
'''
for p in combinations(self.indices_, r=dim - 1):
score = self._calc_score(X_train.values, X_test.values, y_train.values, y_test.values, p)
scores.append(score)
subsets.append(p)
#
# Get the index of best score
#
best_score_index = np.argmax(scores)
#
# Record the best score
#
self.scores_.append(scores[best_score_index])
#
# Get the indices of features which gave best score
#
self.indices_ = subsets[best_score_index]
#
# Record the indices of features for best score
#
self.subsets_.append(self.indices_)
dim -= 1 # Dimension is reduced by 1
'''
Transform training, test data set to the data set
havng features which gave best score
'''
def transform(self, X):
return X.values[:, self.indices_]
'''
Train models with specific set of features
indices - indices of features
'''
def _calc_score(self, X_train, X_test, y_train, y_test, indices):
self.estimator.fit(X_train[:, indices], y_train.ravel())
y_pred = self.estimator.predict(X_test[:, indices])
score = accuracy_score(y_test, y_pred)
return score
# ===============================================
# Keras deep neural network
def dnn():
model = keras.Sequential([
layers.Dense(20, activation='relu', input_shape = (X_train.shape[1])),
layers.Dropout(0.3),
layers.Dense(20, activation='relu'),
layers.Dropout(0.3),
layers.Dense(1, activation='sigmoid'),
])
model.compile(
optimizer='adam',
loss='binary_crossentropy',
metrics=['binary_accuracy']
)
early_stopping = keras.callbacks.EarlyStopping(
patience=5,
min_delta=0.001,
restore_best_weights=True,
)
history = model.fit(
X_train, y_train,
validation_data=(X_test, y_test),
batch_size=512,
callbacks=[early_stopping],
)
history_df = pd.DataFrame(history.history)
print("Minimum Validation Loss: {:0.4f}".format(history_df['val_loss'].min()));
history_df.loc[:, ['loss', 'val_loss']].plot(title="Cross-entropy")
history_df.loc[:, ['binary_accuracy', 'val_binary_accuracy']].plot(title="Accuracy")
return model
keras_clf = KerasClassifier(dnn,
epochs=5,
verbose=False)
keras_clf._estimator_type = "classifier"
And this is the code I have for trying to integrate them together:
k_features = 5
#
# Instantiate SequentialBackwardSearch
#
sbs = SequentialBackwardSearch(keras_clf, k_features)
#
# Fit the data to determine the k_features which give the
# most optimal model performance
#
sbs.fit(X_train, X_test, y_train, y_test)
#
# Transform the training data set to dataset having k_features
# giving most optimal model performance
#
X_train_kfeatures = sbs.transform(X_train)
#
# Transform the test data set to dataset having k_features
#
X_test_kfeatures = sbs.transform(X_test)
sbs.indices_
X_train.columns[[sbs.indices_]] # sbs is an instance of SequentialBackwardSearch class
I am wondering whether this is even possible (integrating a neural network to the existing code for sequential backward feature selection) or if there's anything I can do to get it to run and output the top 5 features from the training dataset. I have tried to address the error message by altering the input shape of the neural network, but I believe it is correct (45 features). Any help or advice would be welcome!

This should work with SciKeras!
I had to clean up your code / fix some bugs. I first did a "sanity check" using Scikit-Learn's MLPClassfier, then I ran it against an MLPClassfier created using Keras. Details may differ for more complex model architectures, but this shows that it does work.
import numpy as np
# SBS (sequential backward feature selection) from scratch
#=====================================================
from sklearn.metrics import accuracy_score
from itertools import combinations
from sklearn.base import clone
class SequentialBackwardSearch:
'''
Instantiate with Estimator and given number of features
'''
def __init__(self, estimator, k_features):
self.estimator = clone(estimator)
self.k_features = k_features
'''
X_train - Training data Pandas dataframe
X_test - Test data Pandas dataframe
y_train - Training label Pandas dataframe
y_test - Test data Pandas dataframe
'''
def fit(self, X_train, X_test, y_train, y_test):
dim = X_train.shape[1]
self.indices_ = tuple(range(dim))
self.subsets_ = [self.indices_]
score = self._calc_score(X_train, X_test,
y_train, y_test, self.indices_)
self.scores_ = [score]
'''
Iterate through all the dimensions until k_features is reached
At the end of loop, dimension count is reduced by 1
'''
while dim > self.k_features:
scores = []
subsets = []
'''
Iterate through different combinations of features, train the model,
record the score
'''
for p in combinations(self.indices_, r=dim - 1):
score = self._calc_score(X_train, X_test, y_train, y_test, p)
scores.append(score)
subsets.append(p)
#
# Get the index of best score
#
best_score_index = np.argmax(scores)
#
# Record the best score
#
self.scores_.append(scores[best_score_index])
#
# Get the indices of features which gave best score
#
self.indices_ = subsets[best_score_index]
#
# Record the indices of features for best score
#
self.subsets_.append(self.indices_)
dim -= 1 # Dimension is reduced by 1
'''
Transform training, test data set to the data set
havng features which gave best score
'''
def transform(self, X):
return X.values[:, self.indices_]
'''
Train models with specific set of features
indices - indices of features
'''
def _calc_score(self, X_train, X_test, y_train, y_test, indices):
self.estimator.fit(X_train[:, indices], y_train.ravel())
y_pred = self.estimator.predict(X_test[:, indices])
score = accuracy_score(y_test, y_pred)
return score
# Sklearn MLPClassifier
from sklearn.neural_network import MLPClassifier
estimator = MLPClassifier()
search = SequentialBackwardSearch(estimator, 1)
X = np.random.randint(0, 2, size=(100, 5))
y = X[:, -1]
search.fit(X, X, y, y)
assert list(search.indices_) == [4]
# SciKeras MLPClassifier
# see https://www.adriangb.com/scikeras/stable/notebooks/MLPClassifier_MLPRegressor.html
import tensorflow.keras as keras
from scikeras.wrappers import KerasClassifier
class KerasMLPClassifier(KerasClassifier):
def __init__(
self,
hidden_layer_sizes=(100, ),
optimizer="adam",
optimizer__learning_rate=0.001,
epochs=200,
verbose=0,
**kwargs,
):
super().__init__(**kwargs)
self.hidden_layer_sizes = hidden_layer_sizes
self.optimizer = optimizer
self.epochs = epochs
self.verbose = verbose
def _keras_build_fn(self, compile_kwargs):
model = keras.Sequential()
inp = keras.layers.Input(shape=(self.n_features_in_))
model.add(inp)
for hidden_layer_size in self.hidden_layer_sizes:
layer = keras.layers.Dense(hidden_layer_size, activation="relu")
model.add(layer)
if self.target_type_ == "binary":
n_output_units = 1
output_activation = "sigmoid"
loss = "binary_crossentropy"
elif self.target_type_ == "multiclass":
n_output_units = self.n_classes_
output_activation = "softmax"
loss = "sparse_categorical_crossentropy"
else:
raise NotImplementedError(f"Unsupported task type: {self.target_type_}")
out = keras.layers.Dense(n_output_units, activation=output_activation)
model.add(out)
model.compile(loss=loss, optimizer=compile_kwargs["optimizer"])
return model
estimator2 = KerasMLPClassifier()
search2 = SequentialBackwardSearch(estimator2, 1)
search2.fit(X, X, y, y)
assert list(search2.indices_) == [4]
Notebook version (can't promise this will be around forever): https://colab.research.google.com/drive/1EWxT3GWZsqhftz4f7W5GsXNe_SPtva4H#scrollTo=chU7wLn1BTU1

Predicting the square root of a number using Machine Learning

I am trying to create a program in python that uses machine learning to predict the square root of a number. I am listing what all I have done in my program:-
created a csv file with numbers and their squares
extracted the data from csv into suitable variables (X stores squares, y stores numbers)
scaled the data using sklearn's, StandardScaler
built the ANN with two hidden layers each of 6 units (no activation functions)
compiled the ANN using SGD as the optimizer and mean squared error as the loss function
trained the model. Loss was around 0.063
tried predicting but the result is something else.
My actual code:-
import numpy as np
import tensorflow as tf
import pandas as pd
df = pd.read_csv('CSV/SQUARE-ROOT.csv')
X = df.iloc[:, 1].values
X = X.reshape(-1, 1)
y = df.iloc[:, 0].values
y = y.reshape(-1, 1)
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0, test_size=0.2)
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_test_sc = sc.fit_transform(X_test)
X_train_sc = sc.fit_transform(X_train)
sc1 = StandardScaler()
y_test_sc1 = sc1.fit_transform(y_test)
y_train_sc1 = sc1.fit_transform(y_train)
ann = tf.keras.models.Sequential()
ann.add(tf.keras.layers.Dense(units=6))
ann.add(tf.keras.layers.Dense(units=6))
ann.add(tf.keras.layers.Dense(units=1))
ann.compile(optimizer='SGD', loss=tf.keras.losses.MeanSquaredError())
ann.fit(x = X_train_sc, y = y_train_sc1, batch_size=5, epochs = 100)
print(sc.inverse_transform(ann.predict(sc.fit_transform([[144]]))))
OUTPUT:- array([[143.99747]], dtype=float32)
Shouldn't the output be 12? Why is it giving me the wrong result?
I am attaching the csv file I used to train my model as well: SQUARE-ROOT.csv

TL;DR: You really need those nonlinearities.
The reason behind it not working could be one (or a combination) of several causes, like bad input data range, flaws in your data, over/underfitting, etc.
However, in this specific case the model you build literally can't learn the function you're trying to approximate, because not having nonlinearities makes this a purely linear model, which can't accurately approximate nonlinear functions.
A Dense layer is implemented as follows:
x_res = activ_func(w*x + b)
where x is the layer input, w the weights, b the bias vector and activ_func the activation function (if one is defined).
Your model, then, mathematically becomes (I'm using indices 1 to 3 for the three Dense layers):
pred = w3 * (w2 * ( w1 * x + b1 ) + b2 ) + b3
= w3*w2*w1*x + w3*w2*b1 + w3*b2 + b3
As you see, the resulting model is still linear.
Add activation functions and your mode becomes capable of learning nonlinear functions too. From there, experiment with the hyperparameters and see how the performance of your model changes.

The reason your code does not work is because you apply fit_transform to your test set, which is wrong. You can fix it by replacing fit_transform(test) to transform(test). Although I don't think StandardScaler is neccessary, please try this:
import numpy as np
import tensorflow as tf
import pandas as pd
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
N = 10000
X = np.arange(1, N).reshape(-1, 1)
y = np.sqrt(X)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0, test_size=0.2)
sc = StandardScaler()
X_train_sc = sc.fit_transform(X_train)
#X_test_sc = sc.fit_transform(X_test) # wrong!!!
X_test_sc = sc.transform(X_test)
sc1 = StandardScaler()
y_train_sc1 = sc1.fit_transform(y_train)
#y_test_sc1 = sc1.fit_transform(y_test) # wrong!!!
y_test_sc1 = sc1.transform(y_test)
ann = tf.keras.models.Sequential()
ann.add(tf.keras.layers.Dense(units=32, activation='relu')) # you have 10000 data, maybe you need a little deeper network
ann.add(tf.keras.layers.Dense(units=32, activation='relu'))
ann.add(tf.keras.layers.Dense(units=32, activation='relu'))
ann.add(tf.keras.layers.Dense(units=1))
ann.compile(optimizer='SGD', loss='MSE')
ann.fit(x=X_train_sc, y=y_train_sc1, batch_size=32, epochs=100, validation_data=(X_test_sc, y_test_sc1))
#print(sc.inverse_transform(ann.predict(sc.fit_transform([[144]])))) # wrong!!!
print(sc1.inverse_transform(ann.predict(sc.transform([[144]]))))

Tensorflow How to correctly pass input values for prediction to the neural network

I'm having trouble while trying to pass values for prediction to my neural network. Here is the code snippet:-
model=keras.Sequential([keras.layers.Dense(units=1, input_shape=[14])])
model.compile(optimizer='sgd', loss='mean_squared_error')
Notice my input_shape=[14]
I'm getting errors while trying to make predictions using each of the following ways:-
print(model.predict(40,8,1,2,0,2,6,10,34,40,16,23,67,25))
TypeError: predict() takes from 2 to 9 positional arguments but 15 were given
print(model.predict([40,8,1,2,0,2,6,10,34,40,16,23,67,25]))
ValueError: Error when checking input: expected dense_1_input to have shape (14,) but got array with shape (1,)
print(model.predict([[40,8,1,2,0,2,6,10,34,40,16,23,67,25]]))
ValueError: Error when checking input: expected dense_1_input to have shape (14,) but got array with shape (1,)
print(model.predict[(40,8,1,2,0,2,6,10,34,40,16,23,67,25)])
TypeError: 'method' object is not subscriptable
print(model.predict([40],[8],[1],[2],[0],[2],[6],[10],[34],[40],[16],[23],[67],[25]))
TypeError: predict() takes from 2 to 9 positional arguments but 15 were given
However, it works with the following way:-
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size= 0.2, shuffle=True)
pred=model.predict(X_test)
Here is a screenshot of X_test when printed print(X_test)
And this is a snippet of my dataset:-
And here is the entire code:-
import glob
import os
from keras.models import Sequential, load_model
import numpy as np
import pandas as pd
from keras.layers import Dense
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder, MinMaxScaler
import matplotlib.pyplot as plt
import keras as k
import tensorflow as tf
from tensorflow import keras
from tensorflow import lite
df = pd.read_csv("kidney4.csv")
df = df.dropna(axis=0)
for column in df.columns:
if df[column].dtype == np.number:
continue
df[column] = LabelEncoder().fit_transform(df[column])
X = df.drop(["classification"], axis=1)
y = df["classification"]
x_scaler = MinMaxScaler()
x_scaler.fit(X)
column_names = X.columns
X[column_names] = x_scaler.transform(X)
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size= 0.2, shuffle=True)
model=keras.Sequential([keras.layers.Dense(units=1, input_shape=[14])])
model.compile(optimizer='sgd', loss='mean_squared_error')
model.fit(X_train, y_train, epochs=500)
for model_file in glob.glob("kidney_final_2.model"):
print("Model file: ", model_file)
model = load_model(model_file)
pred=model.predict(X_test)
pred = [1 if y>=0.5 else 0 for y in pred] #Threshold, transforming probabilities to either 0 or 1 depending if the probability is below or above 0.5
scores = model.evaluate(X_test, y_test)
print()
print("Original : {0}".format(", ".join([str(x) for x in y_test])))
print()
print("Predicted : {0}".format(", ".join([str(x) for x in pred])))
print()
print("Scores : loss = ", scores[0], " acc = ", scores[1])
print("---------------------------------------------------------")
print()
I would appreciate any help on this. Thank you.

Good question.
The problem/trick with the "model.predict()" in Keras and TensorFlow is that you can only predict on batches.
Therefore, in order to predict on one data point(in your case an array of 14 elements), you need to simulate the batch axis. That is, a batch of size 1, since you want to predict on one data point.
You can use numpy to achieve this.
input_array = np.array([1,2,3,4,5,6,7,8,9,10,11,12,13,14])
input_array_for_prediction = np.expand_dims(input_array,axis=0)
print(model.predict(input_array_for_prediction))