maltseasy

Keybase proof

I hereby claim:

• I am aryanmisra on github.
• I am malteasy (https://keybase.io/malteasy) on keybase.
• I have a public key ASATpYrHN8vMcQtXOCMOP4RjRn8yXTaWD3mtt9vpeOOtTwo

To claim this, I am signing this object:

maltseasy

encoder_inputs = Input(shape=(None, num_encoder_tokens))
encoder = LSTM(latent_dim, return_state=True)
encoder_outputs, state_h, state_c = encoder(encoder_inputs)
# We discard `encoder_outputs` and only keep the states.
encoder_states = [state_h, state_c]

# Set up the decoder, using `encoder_states` as initial state.
decoder_inputs = Input(shape=(None, num_decoder_tokens))
# We set up our decoder to return full output sequences,
# and to return internal states as well. We don't use the

maltseasy

input_texts = []
target_texts = []
input_characters = set()
target_characters = set()
with open(data_path, 'r', encoding='utf-8') as f:
    lines = f.read().split('\n')
for line in lines[: min(num_samples, len(lines) - 1)]:
    input_text, target_text = line.split('\t')
    # We use "tab" as the "start sequence" character
    # for the targets, and "\n" as "end sequence" character.

maltseasy

from __future__ import print_function

from keras.models import Model
from keras.layers import Input, LSTM, Dense
import numpy as np

batch_size = 64  # Batch size for training.
epochs = 100  # Number of epochs to train for.
latent_dim = 256  # Latent dimensionality of the encoding space.
num_samples = 10000  # Number of samples to train on.

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filepath = "model.h5"

# Declare a checkpoint to save the best version of the model
checkpoint = ModelCheckpoint(filepath, monitor='val_top_3_accuracy', verbose=1,
                             save_best_only=True, mode='max')

# Reduce the learning rate as the learning stagnates
reduce_lr = ReduceLROnPlateau(monitor='val_top_3_accuracy', factor=0.5, patience=2,
                              verbose=1, mode='max', min_lr=0.00001)

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# Define Top2 and Top3 Accuracy
from keras.metrics import categorical_accuracy, top_k_categorical_accuracy

def top_3_accuracy(y_true, y_pred):
    return top_k_categorical_accuracy(y_true, y_pred, k=3)

def top_2_accuracy(y_true, y_pred):
    return top_k_categorical_accuracy(y_true, y_pred, k=2)

# Compile the model

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# Create a MobileNet model
mobile = keras.applications.mobilenet.MobileNet()

# Modify the model
# Choose the 6th layer from the last
x = mobile.layers[-6].output

# Add a dropout and dense layer for predictions
x = Dropout(0.25)(x)
predictions = Dense(7, activation='softmax')(x)

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# Set up generators
train_batches = ImageDataGenerator(
    preprocessing_function= \
        keras.applications.mobilenet.preprocess_input).flow_from_directory(
    train_path, #pointing to the path where the data is stored
    target_size=(image_size, image_size), #the target size of the image output
    batch_size=train_batch_size) #size of the batches

valid_batches = ImageDataGenerator(
    preprocessing_function= \

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# The paths for the training and validation images
train_path = 'base_dir/train_dir'
valid_path = 'base_dir/val_dir'

# Declare a few useful values

#number of images in the training set
num_train_samples = 9013
#number of images in the evaluation set
num_val_samples = 1002

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# Import the libraries
import numpy as np
import keras
from keras import backend as K
from keras.layers.core import Dense, Dropout
from keras.optimizers import Adam
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Model
from keras.callbacks import ReduceLROnPlateau, ModelCheckpoint
from sklearn.metrics import confusion_matrix