I try to run the churn_modeling.csv file in Keras but I don't the model learning. Here is my code:
# -*- coding: utf-8 -*-
import keras
from keras.models import Sequential
from keras.layers import Dense
from keras.optimizers import SGD
import pandas as pd
import numpy as np
#read from CSV file ,convert categorial value to one-hot-encoding and convert the result to numpy array
df=pd.read_csv("churn_modelling.csv")
X=pd.get_dummies(df, columns=['Geography','Gender'])
X=X[['CreditScore','Age','Tenure','Balance','NumOfProducts','HasCrCard','IsActiveMember','EstimatedSalary','Geography_France','Geography_Germany','Geography_Spain','Gender_Female','Gender_Male','Exited']]
dataset=X.as_matrix()
X_train=dataset[:,0:13]
Y_train=dataset[:,13]
model=Sequential()
model.add(Dense(26, input_dim=13, activation='relu'))
#model.add(Dense(15, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
sgd = SGD(lr=0.02)
model.compile(loss='binary_crossentropy', optimizer=sgd, metrics=['accuracy'])
model.fit(X_train,Y_train, validation_split=0.05, epochs=10, batch_size=200)
This is the output I got:
Train on 9500 samples, validate on 500 samples
Epoch 1/10
9500/9500 [==============================] - 0s - loss: 3.7996 - acc: 0.7637 - val_loss: 2.8045 - val_acc: 0.8260
Epoch 2/10
9500/9500 [==============================] - 0s - loss: 3.3085 - acc: 0.7947 - val_loss: 2.8045 - val_acc: 0.8260
Epoch 3/10
9500/9500 [==============================] - 0s - loss: 3.3085 - acc: 0.7947 - val_loss: 2.8045 - val_acc: 0.8260
Epoch 4/10
9500/9500 [==============================] - 0s - loss: 3.3085 - acc: 0.7947 - val_loss: 2.8045 - val_acc: 0.8260
Epoch 5/10
9500/9500 [==============================] - 0s - loss: 3.3085 - acc: 0.7947 - val_loss: 2.8045 - val_acc: 0.8260
Epoch 6/10
9500/9500 [==============================] - 0s - loss: 3.3085 - acc: 0.7947 - val_loss: 2.8045 - val_acc: 0.8260
Epoch 7/10
9500/9500 [==============================] - 0s - loss: 3.3085 - acc: 0.7947 - val_loss: 2.8045 - val_acc: 0.8260
Epoch 8/10
9500/9500 [==============================] - 0s - loss: 3.3085 - acc: 0.7947 - val_loss: 2.8045 - val_acc: 0.8260
Epoch 9/10
9500/9500 [==============================] - 0s - loss: 3.3085 - acc: 0.7947 - val_loss: 2.8045 - val_acc: 0.8260
Epoch 10/10
9500/9500 [==============================] - 0s - loss: 3.3085 - acc: 0.7947 - val_loss: 2.8045 - val_acc: 0.8260
Even if I run the program with 100 epochs, I still get the same result val_acc: 0.8260. Thank you
It seems that if training set is rescaled, the accuracy improves slightly 86%. I have used the following code:
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler(feature_range=(0, 1))
rescaledX_train = scaler.fit_transform(X_train)
Related
I am trying to classify the severity of COVID XRay using 426 256x256 xray images and 4 classes present. However the validation accuracy doesnt improve at all. The validation loss also barely decreases from the start
This is the model I am using
from keras.models import Sequential
from keras.layers import Dense,Conv2D,MaxPooling2D,Dropout,Flatten
from tensorflow.keras.callbacks import EarlyStopping
from tensorflow.keras import regularizers
model=Sequential()
model.add(Conv2D(filters=64,kernel_size=(4,4),input_shape=image_shape,activation="relu"))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Dropout(0.2))
model.add(Conv2D(filters=128,kernel_size=(6,6),input_shape=image_shape,activation="relu"))
model.add(MaxPooling2D(pool_size=(3,3)))
model.add(Dropout(0.2))
model.add(Flatten())
model.add(Dense(64,activation="relu"))
model.add(Dense(16,activation="relu"))
model.add(Dense(4,activation="softmax"))
model.compile(loss="categorical_crossentropy",optimizer="adam",metrics=["accuracy"])
These are the outputs I get
epochs = 20
batch_size = 8
model.fit(X_train, y_train, validation_data=(X_test, y_test),
epochs=epochs,
batch_size=batch_size
)
Epoch 1/20
27/27 [==============================] - 4s 143ms/step - loss: 0.1776 - accuracy: 0.9528 - val_loss: 3.7355 - val_accuracy: 0.2717
Epoch 2/20
27/27 [==============================] - 4s 142ms/step - loss: 0.1152 - accuracy: 0.9481 - val_loss: 4.0038 - val_accuracy: 0.2283
Epoch 3/20
27/27 [==============================] - 4s 142ms/step - loss: 0.0875 - accuracy: 0.9858 - val_loss: 4.1756 - val_accuracy: 0.2391
Epoch 4/20
27/27 [==============================] - 4s 142ms/step - loss: 0.0521 - accuracy: 0.9906 - val_loss: 4.1034 - val_accuracy: 0.2717
Epoch 5/20
27/27 [==============================] - 4s 142ms/step - loss: 0.0496 - accuracy: 0.9858 - val_loss: 4.8433 - val_accuracy: 0.3152
Epoch 6/20
27/27 [==============================] - 4s 142ms/step - loss: 0.0170 - accuracy: 0.9953 - val_loss: 5.6027 - val_accuracy: 0.3043
Epoch 7/20
27/27 [==============================] - 4s 142ms/step - loss: 0.2307 - accuracy: 0.9245 - val_loss: 4.2759 - val_accuracy: 0.3152
Epoch 8/20
27/27 [==============================] - 4s 142ms/step - loss: 0.6493 - accuracy: 0.7830 - val_loss: 3.8390 - val_accuracy: 0.3478
Epoch 9/20
27/27 [==============================] - 4s 142ms/step - loss: 0.2563 - accuracy: 0.9009 - val_loss: 5.0250 - val_accuracy: 0.2500
Epoch 10/20
27/27 [==============================] - 4s 142ms/step - loss: 0.0286 - accuracy: 1.0000 - val_loss: 4.6475 - val_accuracy: 0.2391
Epoch 11/20
27/27 [==============================] - 4s 142ms/step - loss: 0.0097 - accuracy: 1.0000 - val_loss: 5.2198 - val_accuracy: 0.2391
Epoch 12/20
27/27 [==============================] - 4s 142ms/step - loss: 0.0037 - accuracy: 1.0000 - val_loss: 5.7914 - val_accuracy: 0.2500
Epoch 13/20
27/27 [==============================] - 4s 142ms/step - loss: 0.0048 - accuracy: 1.0000 - val_loss: 5.4341 - val_accuracy: 0.2391
Epoch 14/20
27/27 [==============================] - 4s 142ms/step - loss: 0.0044 - accuracy: 1.0000 - val_loss: 5.6364 - val_accuracy: 0.2391
Epoch 15/20
27/27 [==============================] - 4s 143ms/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 5.8504 - val_accuracy: 0.2391
Epoch 16/20
27/27 [==============================] - 4s 143ms/step - loss: 0.0013 - accuracy: 1.0000 - val_loss: 5.9604 - val_accuracy: 0.2500
Epoch 17/20
27/27 [==============================] - 4s 149ms/step - loss: 0.0023 - accuracy: 1.0000 - val_loss: 6.0851 - val_accuracy: 0.2717
Epoch 18/20
27/27 [==============================] - 4s 142ms/step - loss: 0.0134 - accuracy: 0.9953 - val_loss: 4.9783 - val_accuracy: 0.2717
Epoch 19/20
27/27 [==============================] - 4s 141ms/step - loss: 0.0068 - accuracy: 1.0000 - val_loss: 5.7421 - val_accuracy: 0.2500
Epoch 20/20
27/27 [==============================] - 4s 142ms/step - loss: 0.0024 - accuracy: 1.0000 - val_loss: 5.8480 - val_accuracy: 0.2283
Any tips on how i can solve this or If i am doing something wrong?
I Have created a model to classify plane and cars images bu after very epoch the acc and val_acc remains same
import numpy as np
import matplotlib as plt
from keras.models import Sequential
from keras.layers import Convolution2D
from keras.layers import MaxPooling2D
from keras.layers import Flatten
from keras.layers import Dense
from keras.preprocessing.image import ImageDataGenerator
from keras.preprocessing.image import image
import os
model=Sequential()
model.add(Convolution2D(32,(3,3),input_shape=(64,64,3),activation="relu"))
model.add(MaxPooling2D(2,2))
model.add(Convolution2D(64,(3,3),activation="relu"))
model.add(MaxPooling2D(2,2))
model.add(Convolution2D(64,(3,3),activation="sigmoid"))
model.add(MaxPooling2D(2,2))
model.add(Flatten())
model.add(Dense(32,activation="sigmoid"))
model.add(Dense(32,activation="sigmoid"))
model.add(Dense(32,activation="sigmoid"))
model.add(Dense(1,activation="softmax"))
model.compile(optimizer='adam', loss='binary_crossentropy',
metrics=['accuracy'])
train_datagen = ImageDataGenerator(
rescale=1./255,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True)
test_datagen = ImageDataGenerator(rescale=1./255)
train_set = train_datagen.flow_from_directory(
'train_images',
target_size=(64,64),
batch_size=32,
class_mode='binary')
test_set = train_datagen.flow_from_directory(
'val_set',
target_size=(64,64),
batch_size=32,
class_mode='binary')
model.fit_generator(
train_set,
steps_per_epoch=160,
epochs=25,
validation_data=test_set,
validation_steps=40)
Epoch 1/25
30/30 [==============================] - 18s 593ms/step - loss: 7.9712 - acc: 0.5000 - val_loss:
7.9712 - val_acc: 0.5000
Epoch 2/25
30/30 [==============================] - 15s 491ms/step - loss: 7.9712 - acc: 0.5000 - val_loss:
7.9712 - val_acc: 0.5000
Epoch 3/25
30/30 [==============================] - 19s 640ms/step - loss: 7.9712 - acc: 0.5000 - val_loss:
7.9712 - val_acc: 0.5000
Epoch 4/25
30/30 [==============================] - 14s 474ms/step - loss: 7.9712 - acc: 0.5000 - val_loss:
7.9712 - val_acc: 0.5000
Epoch 5/25
30/30 [==============================] - 16s 532ms/step - loss: 7.9712 - acc: 0.5000 - val_loss:
7.9712 - val_acc: 0.5000
Epoch 6/25
30/30 [==============================] - 14s 473ms/step - loss: 7.9712 - acc: 0.5000 - val_loss:
7.9712 - val_acc: 0.5000
Epoch 7/25
30/30 [==============================] - 14s 469ms/step - loss: 7.9712 - acc: 0.5000 - val_loss:
7.9712 - val_acc: 0.5000
Epoch 8/25
30/30 [==============================] - 14s 469ms/step - loss: 7.9712 - acc: 0.5000 - val_loss:
7.9712 - val_acc: 0.5000
Epoch 9/25
30/30 [==============================] - 14s 472ms/step - loss: 7.9712 - acc: 0.5000 - val_loss:
7.9712 - val_acc: 0.5000
Epoch 10/25
30/30 [==============================] - 16s 537ms/step - loss: 7.9712 - acc: 0.5000 - val_loss:
7.9712 - val_acc: 0.5000
Epoch 11/25
30/30 [==============================] - 18s 590ms/step - loss: 7.9712 - acc: 0.5000 - val_loss:
7.9712 - val_acc: 0.5000
Epoch 12/25
30/30 [==============================] - 13s 441ms/step - loss: 7.9712 - acc: 0.5000 - val_loss:
7.9712 - val_acc: 0.5000
Epoch 13/25
30/30 [==============================] - 11s 374ms/step - loss: 7.9712 - acc: 0.5000 - val_loss:
7.9712 - val_acc: 0.5000
Epoch 14/25
30/30 [==============================] - 11s 370ms/step - loss: 7.9712 - acc: 0.5000 - val_loss:
7.9712 - val_acc: 0.5000
Epoch 15/25
30/30 [==============================] - 13s 441ms/step - loss: 7.9712 - acc: 0.5000 - val_loss:
7.9712 - val_acc: 0.5000
Epoch 16/25
30/30 [==============================] - 13s 419ms/step - loss: 7.9712 - acc: 0.5000 - val_loss:
7.9712 - val_acc: 0.5000
Epoch 17/25
30/30 [==============================] - 12s 401ms/step - loss: 7.9712 - acc: 0.5000 - val_loss:
7.9712 - val_acc: 0.5000
Epoch 18/25
30/30 [==============================] - 16s 536ms/step - loss: 7.9712 - acc: 0.5000 - val_loss:
7.9712 - val_acc: 0.5000
Epoch 19/25
30/30 [==============================] - 16s 523ms/step - loss: 7.9712 - acc: 0.5000 - val_loss:
7.9712 - val_acc: 0.5000
Epoch 20/25
30/30 [==============================] - 16s 530ms/step - loss: 7.9712 - acc: 0.5000 - val_loss:
7.9712 - val_acc: 0.5000
Epoch 21/25
30/30 [==============================] - 16s 546ms/step - loss: 7.9712 - acc: 0.5000 - val_loss:
7.9712 - val_acc: 0.5000
Epoch 22/25
30/30 [==============================] - 15s 500ms/step - loss: 7.9712 - acc: 0.5000 - val_loss:
7.9712 - val_acc: 0.5000
Epoch 23/25
30/30 [==============================] - 16s 546ms/step - loss: 7.9712 - acc: 0.5000 - val_loss:
7.9712 - val_acc: 0.5000
Epoch 24/25
30/30 [==============================] - 16s 545ms/step - loss: 7.9712 - acc: 0.5000 - val_loss:
7.9712 - val_acc: 0.5000
Epoch 25/25
30/30 [==============================] - 15s 515ms/step - loss: 7.9712 - acc: 0.5000 - val_loss:
7.9712 - val_acc: 0.5000
You have several issues in your model structure.
First of all, for the output of your model
model.add(Dense(1,activation="softmax"))
You are using a softmax, which means you try to solve a multi-class classification, not a binary classification. If it is really the case, you need to change your loss to categorical_crossentropy. In this way the compile line will turn into:
model.compile(optimizer='adam', loss='categorical_crossentropy',
metrics=['accuracy'])
If it is not the case and you want only solve a binary classification, you might be good, but I do suggest to change the last layer activation to sigmoid
Second: That is a bad idea to use sigmoid as the activation in the middle layer since it can easily cause the gradient to vanish (read more here ). Try to change all the sigmoid activation in the middle layer with wether relu or even better with leakyrelu
The problem is exactly here:
model.add(Dense(1,activation="softmax"))
You cannot use softmax with one neuron, as it normalizes over neurons, meaning that with one neuron it will always produce a constant 1.0 value. For binary classification you have to use sigmoid activation at the output:
model.add(Dense(1,activation="sigmoid"))
Also it is not wise to use sigmoid activations in hidden layers, as they will produce vanishing gradient problems. Please prefer ReLU or similar activations.
I want to build a model using Keras to predict the price of the natural gas.
The dataset contains the price for the gas daily and monthly since 1997 and it is available Here.
The following graph shows the prices during a sequence of days. X is days and Y is the price.
I have tried LSTM with 4,50,100 cell in hidden layer but the accuracy still not was bad and the model failed to predict future price.
I have added another two hidden layers (full connected) with 100 and 128 cell but it did not work too.
This is the model and the result form training process:
num_units = 100
activation_function = 'sigmoid'
optimizer = 'adam'
loss_function = 'mean_squared_error'
batch_size = 5
num_epochs = 10
log_file_name = f"{SEQ_LEN}-SEQ-{1}-PRED-{int(time.time())}"
# Initialize the model (of a Sequential type)
model = Sequential()
# Adding the input layer and the LSTM layer
model.add(LSTM(units = num_units, activation = activation_function,input_shape=(None, 1)))
# Adding the output layer
model.add(Dense(units = 1))
# Compiling the RNN
model.compile(optimizer = optimizer, loss = loss_function, metrics=['accuracy'])
# Using the training set to train the model
history = model.fit(train_x, train_y, batch_size = batch_size, epochs =num_epochs,validation_data=(test_x, test_y))
and the output is :
Train on 4362 samples, validate on 1082 samples
Epoch 1/10
4362/4362 [==============================] - 11s 3ms/step - loss: 0.0057 - acc: 2.2925e-04 - val_loss: 0.0016 - val_acc: 0.0018
Epoch 2/10
4362/4362 [==============================] - 9s 2ms/step - loss: 6.2463e-04 - acc: 4.5851e-04 - val_loss: 0.0013 - val_acc: 0.0018
Epoch 3/10
4362/4362 [==============================] - 9s 2ms/step - loss: 6.1073e-04 - acc: 2.2925e-04 - val_loss: 0.0014 - val_acc: 0.0018
Epoch 4/10
4362/4362 [==============================] - 8s 2ms/step - loss: 5.4403e-04 - acc: 4.5851e-04 - val_loss: 0.0014 - val_acc: 0.0018
Epoch 5/10
4362/4362 [==============================] - 7s 2ms/step - loss: 5.4765e-04 - acc: 4.5851e-04 - val_loss: 0.0012 - val_acc: 0.0018
Epoch 6/10
4362/4362 [==============================] - 8s 2ms/step - loss: 5.1991e-04 - acc: 4.5851e-04 - val_loss: 0.0013 - val_acc: 0.0018
Epoch 7/10
4362/4362 [==============================] - 7s 2ms/step - loss: 5.7324e-04 - acc: 2.2925e-04 - val_loss: 0.0011 - val_acc: 0.0018
Epoch 8/10
4362/4362 [==============================] - 7s 2ms/step - loss: 4.4248e-04 - acc: 4.5851e-04 - val_loss: 0.0011 - val_acc: 0.0018
Epoch 9/10
4362/4362 [==============================] - 7s 2ms/step - loss: 4.3868e-04 - acc: 4.5851e-04 - val_loss: 0.0011 - val_acc: 0.0018
Epoch 10/10
4362/4362 [==============================] - 7s 2ms/step - loss: 4.6654e-04 - acc: 4.5851e-04 - val_loss: 0.0011 - val_acc: 0.0018
How to know the number of layers and cells for problem like this? Anyone can suggest a netwrok structure that can solve this problem?
I have 3D CNN U-net architecture to solve segmentation problem. I am using Adam optimisation together with binary cross entropy and the metric is "accuracy". I try to understand why it does not improve.
Train on 2774 samples, validate on 694 samples
Epoch 1/20
2774/2774 [==============================] - 166s 60ms/step - loss: 0.5189 - acc: 0.7928 - val_loss: 0.5456 - val_acc: 0.7674
Epoch 00001: val_loss improved from inf to 0.54555, saving model to model-tgs-salt-1.h5
Epoch 2/20
2774/2774 [==============================] - 170s 61ms/step - loss: 0.5170 - acc: 0.7928 - val_loss: 0.5485 - val_acc: 0.7674
Epoch 00002: val_loss did not improve from 0.54555
Epoch 3/20
2774/2774 [==============================] - 169s 61ms/step - loss: 0.5119 - acc: 0.7928 - val_loss: 0.5455 - val_acc: 0.7674
Epoch 00003: val_loss improved from 0.54555 to 0.54549, saving model to model-tgs-salt-1.h5
Epoch 4/20
2774/2774 [==============================] - 170s 61ms/step - loss: 0.5117 - acc: 0.7928 - val_loss: 0.5715 - val_acc: 0.7674
Epoch 00004: val_loss did not improve from 0.54549
Epoch 5/20
2774/2774 [==============================] - 169s 61ms/step - loss: 0.5126 - acc: 0.7928 - val_loss: 0.5566 - val_acc: 0.7674
Epoch 00005: val_loss did not improve from 0.54549
Epoch 6/20
2774/2774 [==============================] - 169s 61ms/step - loss: 0.5138 - acc: 0.7928 - val_loss: 0.5503 - val_acc: 0.7674
Epoch 00006: val_loss did not improve from 0.54549
Epoch 7/20
2774/2774 [==============================] - 170s 61ms/step - loss: 0.5103 - acc: 0.7928 - val_loss: 0.5444 - val_acc: 0.7674
Epoch 00007: val_loss improved from 0.54549 to 0.54436, saving model to model-tgs-salt-1.h5
Epoch 8/20
2774/2774 [==============================] - 169s 61ms/step - loss: 0.5137 - acc: 0.7928 - val_loss: 0.5454 - val_acc: 0.7674
If you use batch size in your network. let's try to increase that. I think it could be useful in speed of train.
I am currently working on a digit recognition challenge by Analytics Vidhya, the link to which is https://datahack.analyticsvidhya.com/contest/practice-problem-identify-the-digits/ .
The images in the dataset pertaining to this challenge are of dimensions 28*28*4 (28 = length = width , 4 = no. of channels).The code I have implemented is:
from keras.models import Sequential
from keras.layers import Dense,Dropout,Flatten,Activation
from keras.layers.convolutional import Conv2D
from keras.layers.convolutional import MaxPooling2D
from keras.utils import np_utils
from keras import backend as K
K.set_image_dim_ordering('th')
import numpy as np
# fix random seed for reproducibility
seed = 7
np.random.seed(seed)
# define the larger model
def larger_model():
# create model
model = Sequential()
model.add(Conv2D(32, (3, 3), input_shape=(4, 28, 28),activation='relu',padding='same'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(15, (3, 3), activation='relu',padding='same'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.2))
model.add(Flatten())
model.add(Dense(200, activation='relu'))
model.add(Dense(num_classes, activation='softmax'))
# Compile model
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
return model
def loadImages(path):
# return array of images
imagesList = listdir(path)
loadedImages = []
for image in imagesList:
img = io.imread(path + "/" + image,as_grey = False)
loadedImages.append(np.array(img))
return loadedImages
path = "C:/Users/Farz Jamal/Downloads/mnist/Train/Images/train" #path_to_train_dataset
import pandas as pd
df = pd.read_csv("C:/Users/Farz Jamal/Downloads/mnist/Train/train.csv") #path_to_class_labels
y = np.array(df['label'])
from sklearn.cross_validation import train_test_split as ttt
x_train,x_val,y_train,y_val = ttt(imgs,y,test_size = 0.2)
Continued Code:
x_vall,x_test,y_vall,y_test = ttt(x_val,y_val,test_size = 0.4)
x_train,x_vall,x_test = np.array(x_train).astype('float32'),np.array(x_vall).astype('float32'),np.array(x_test).astype('float32')
# normalize inputs from 0-255 to 0-1
x_train = x_train / 255.0
x_vall = x_vall / 255.0
x_test = x_test / 255.0
y_train = np_utils.to_categorical(y_train)
y_vall = np_utils.to_categorical(y_vall)
y_test = np_utils.to_categorical(y_test)
num_classes = y_vall.shape[1] #10
#fitting_and_evaluating
model = larger_model()
# Fit the model
model.fit(x_train, y_train, validation_data=(x_vall, y_vall), epochs=50, batch_size=200)
# Final evaluation of the model
scores = model.evaluate(x_test, y_test, verbose=0)
The output is coming as follows:(from 16thepoch to 37th epoch)
Epoch 16/50
39200/39200 [==============================] - 271s 7ms/step - loss: 2.3013 - acc: 0.1135 - val_loss: 2.3015 - val_acc: 0.1095
Epoch 17/50
39200/39200 [==============================] - 275s 7ms/step - loss: 2.3011 - acc: 0.1128 - val_loss: 2.3014 - val_acc: 0.1095
Epoch 18/50
39200/39200 [==============================] - 270s 7ms/step - loss: 2.3011 - acc: 0.1124 - val_loss: 2.3015 - val_acc: 0.1095
Epoch 19/50
39200/39200 [==============================] - 273s 7ms/step - loss: 2.3012 - acc: 0.1131 - val_loss: 2.3017 - val_acc: 0.1095
Epoch 20/50
39200/39200 [==============================] - 273s 7ms/step - loss: 2.3011 - acc: 0.1130 - val_loss: 2.3018 - val_acc: 0.1111
Epoch 21/50
39200/39200 [==============================] - 272s 7ms/step - loss: 2.3010 - acc: 0.1127 - val_loss: 2.3013 - val_acc: 0.1095
Epoch 22/50
39200/39200 [==============================] - 281s 7ms/step - loss: 2.3006 - acc: 0.1133 - val_loss: 2.3015 - val_acc: 0.1097
Epoch 23/50
39200/39200 [==============================] - 273s 7ms/step - loss: 2.3005 - acc: 0.1136 - val_loss: 2.3018 - val_acc: 0.1099
Epoch 24/50
39200/39200 [==============================] - 276s 7ms/step - loss: 2.3005 - acc: 0.1135 - val_loss: 2.3022 - val_acc: 0.1116
Epoch 25/50
39200/39200 [==============================] - 271s 7ms/step - loss: 2.2998 - acc: 0.1155 - val_loss: 2.3025 - val_acc: 0.1071
Epoch 26/50
39200/39200 [==============================] - 271s 7ms/step - loss: 2.2996 - acc: 0.1156 - val_loss: 2.3021 - val_acc: 0.1100
Epoch 27/50
39200/39200 [==============================] - 272s 7ms/step - loss: 2.2981 - acc: 0.1168 - val_loss: 2.3024 - val_acc: 0.1078
Epoch 28/50
39200/39200 [==============================] - 270s 7ms/step - loss: 2.2970 - acc: 0.1187 - val_loss: 2.3035 - val_acc: 0.1065
Epoch 29/50
39200/39200 [==============================] - 271s 7ms/step - loss: 2.2945 - acc: 0.1218 - val_loss: 2.3061 - val_acc: 0.1041
Epoch 30/50
39200/39200 [==============================] - 270s 7ms/step - loss: 2.2935 - acc: 0.1223 - val_loss: 2.3059 - val_acc: 0.1003
Epoch 31/50
39200/39200 [==============================] - 274s 7ms/step - loss: 2.2906 - acc: 0.1268 - val_loss: 2.3067 - val_acc: 0.1014
Epoch 32/50
39200/39200 [==============================] - 276s 7ms/step - loss: 2.2873 - acc: 0.1278 - val_loss: 2.3078 - val_acc: 0.1073
Epoch 33/50
39200/39200 [==============================] - 292s 7ms/step - loss: 2.2806 - acc: 0.1368 - val_loss: 2.3118 - val_acc: 0.1034
Epoch 34/50
39200/39200 [==============================] - 301s 8ms/step - loss: 2.2744 - acc: 0.1404 - val_loss: 2.3160 - val_acc: 0.1022
Epoch 35/50
39200/39200 [==============================] - 289s 7ms/step - loss: 2.2662 - acc: 0.1486 - val_loss: 2.3172 - val_acc: 0.1029
Epoch 36/50
39200/39200 [==============================] - 295s 8ms/step - loss: 2.2557 - acc: 0.1543 - val_loss: 2.3162 - val_acc: 0.1087
Epoch 37/50
39200/39200 [==============================] - 308s 8ms/step - loss: 2.2459 - acc: 0.1632 - val_loss: 2.3275 - val_acc: 0.1083
As can be seen, there is very low training as well validation accuracy.
I have tried reducing Dropout(previously it was 0.5 for one of the layers) but still no effect. I doubled the neurons in the last hidden layer,(previously they were 100), still no effect. It seems like, it is something to do with the pre processing of the images as well as the input parameters for the image.
What can be done?
Copied in from comments as the answer:
In fact your model isn't learning anything, which usually points to a bug. I don't see anything overtly wrong. A common error is inputting garbage to the network accidentally. Take the first few images that you're feeding to the network and display them in a debugger before your fit step and print out the labels and make sure they match. Do a sanity check on your inputs.