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data = data[data.TICKER != 'GEF']
data = data[data.TYPE != 'Intraday']

drop_cols = ['SPLIT_RATIO', 'EX_DIVIDEND', 'ADJ_FACTOR', 'ADJ_VOLUME', 'ADJ_CLOSE', 'ADJ_LOW', 'ADJ_HIGH', 'ADJ_OPEN', 'VOLUME', 'FREQUENCY', 'TYPE', 'FIGI']

data.drop(drop_cols, axis=1, inplace=True)

data.head()

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import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()

from sklearn.metrics import r2_score, median_absolute_error, mean_absolute_error
from sklearn.metrics import median_absolute_error, mean_squared_error, mean_squared_log_error

from scipy.optimize import minimize

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def djmodel(Tx, n_a, n_values):
    
    # Define the input of your model with a shape 
    X = Input(shape=(Tx, n_values))
    
    # Define s0, initial hidden state for the decoder LSTM
    a0 = Input(shape=(n_a,), name='a0')
    c0 = Input(shape=(n_a,), name='c0')
    a = a0
    c = c0

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def model(data, ix_to_char, char_to_ix, num_iterations = 35000, n_a = 50, dino_names = 7, vocab_size = 27):
    
    # Retrieve n_x and n_y from vocab_size
    n_x, n_y = vocab_size, vocab_size
    
    # Initialize parameters
    parameters = initialize_parameters(n_a, n_x, n_y)
    
    # Initialize loss (this is required because we want to smooth our loss, don't worry about it)
    loss = get_initial_loss(vocab_size, dino_names)

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# Reset the graph
tf.reset_default_graph()

# Start interactive session
sess = tf.InteractiveSession()

content_image = scipy.misc.imread("images/louvre_small.jpg")
content_image = reshape_and_normalize_image(content_image)

style_image = scipy.misc.imread("images/monet.jpg")

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def ResNet50(input_shape = (64, 64, 3), classes = 6):
    
    # Define the input as a tensor with shape input_shape
    X_input = Input(input_shape)

    
    # Zero-Padding
    X = ZeroPadding2D((3, 3))(X_input)
    
    # Stage 1

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def model(X_train, Y_train, X_test, Y_test, learning_rate=0.009,
          num_epochs=400, minibatch_size=64, print_cost=True):
    
    ops.reset_default_graph()                         # to be able to rerun the model without overwriting tf variables
    tf.set_random_seed(1)                             # to keep results consistent (tensorflow seed)
    seed = 3                                          # to keep results consistent (numpy seed)
    (m, n_H0, n_W0, n_C0) = X_train.shape             
    n_y = Y_train.shape[1]                            
    costs = []                                        # To keep track of the cost
    

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def model(X_train, Y_train, X_test, Y_test, learning_rate = 0.0001,
          num_epochs = 1500, minibatch_size = 32, print_cost = True):
    
    ops.reset_default_graph()                         # to be able to rerun the model without overwriting tf variables
    tf.set_random_seed(1)                             # to keep consistent results
    seed = 3                                          # to keep consistent results
    (n_x, m) = X_train.shape                          # (n_x: input size, m : number of examples in the train set)
    n_y = Y_train.shape[0]                            # n_y : output size
    costs = []                                        # To keep track of the cost
    

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def model(X, Y, layers_dims, optimizer, learning_rate=0.0007, mini_batch_size=64, beta=0.9,
          beta1=0.9, beta2=0.999, epsilon=1e-8, num_epochs=10000, print_cost=True):

    L = len(layers_dims)    # number of layers in the neural networks
    costs = []              # to keep track of the cost
    t = 0                   # initializing the counter required for Adam update
    seed = 10               
    
    # Initialize parameters
    parameters = initialize_parameters(layers_dims)

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def backward_propagation_with_dropout(X, Y, cache, keep_prob):
    
    m = X.shape[1]
    (Z1, D1, A1, W1, b1, Z2, D2, A2, W2, b2, Z3, A3, W3, b3) = cache
    
    dZ3 = A3 - Y
    dW3 = 1./m * np.dot(dZ3, A2.T)
    db3 = 1./m * np.sum(dZ3, axis=1, keepdims = True)
    dA2 = np.dot(W3.T, dZ3)